library(renv)
library(tidyverse)
library(broom)
library(labelled)
library(psych)
library(GPArotation)
#library(devtools)
#install_github("cran/multicon") # not on CRAN atm
library(multicon)
library(correlation)
library(careless)
library(corrplot)
library(lavaan)
library(semTools)
library(semPlot)
library(knitr)
library(ggdist)
library(ggforce)
library(cowplot)
library(nortest)
library(lmerTest)source("clean_data_st2.R")Reshape data
df_sbsa2_wide_wb <- df_sbsa2 %>%
arrange(pid, time) %>%
select(pid, time, rando, starts_with(c("sw06", "ml01", "rs01", "sc01"))) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = starts_with(c("sw06", "ml01", "rs01", "sc01")))
# colnames(df_sbsa2_wide_wb)Check CFA item loadings at T1
cfa_meaning <- '
# Define the latent factors
meaning1 =~ NA*ml01_01_t1 + lambda1*ml01_01_t1 + lambda2*ml01_02_t1 + lambda3*ml01_03_t1 + lambda4*ml01_04_t1 + lambda5*ml01_05_t1 + lambda6*ml01_06_t1 + lambda7*ml01_07_t1 + lambda8*ml01_08_t1 + lambda9*ml01_09_t1 + lambda10*ml01_10_t1
# Intercepts
ml01_01_t1 ~ i1*1
ml01_02_t1 ~ 1
ml01_03_t1 ~ 1
ml01_04_t1 ~ 1
ml01_05_t1 ~ 1
ml01_06_t1 ~ 1
ml01_07_t1 ~ 1
ml01_08_t1 ~ 1
ml01_09_t1 ~ 1
ml01_10_t1 ~ 1
# Unique Variances
ml01_01_t1 ~~ ml01_01_t1
ml01_02_t1 ~~ ml01_02_t1
ml01_03_t1 ~~ ml01_03_t1
ml01_04_t1 ~~ ml01_04_t1
ml01_05_t1 ~~ ml01_05_t1
ml01_06_t1 ~~ ml01_06_t1
ml01_07_t1 ~~ ml01_07_t1
ml01_08_t1 ~~ ml01_08_t1
ml01_09_t1 ~~ ml01_09_t1
ml01_10_t1 ~~ ml01_10_t1
# Latent Variable Means
meaning1 ~ 0*1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
'
fit_cfa_meaning <- cfa(cfa_meaning, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_cfa_meaning, fit.measures = TRUE)lavaan 0.6.15 ended normally after 23 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Used Total
Number of observations 847 849
Number of missing patterns 1
Model Test User Model:
Test statistic 2451.486
Degrees of freedom 35
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 5903.003
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.587
Tucker-Lewis Index (TLI) 0.470
Robust Comparative Fit Index (CFI) 0.587
Robust Tucker-Lewis Index (TLI) 0.470
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -14719.920
Loglikelihood unrestricted model (H1) -13494.177
Akaike (AIC) 29499.840
Bayesian (BIC) 29642.091
Sample-size adjusted Bayesian (SABIC) 29546.820
Root Mean Square Error of Approximation:
RMSEA 0.286
90 Percent confidence interval - lower 0.276
90 Percent confidence interval - upper 0.295
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.286
90 Percent confidence interval - lower 0.276
90 Percent confidence interval - upper 0.295
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.222
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
meaning1 =~
m01_01_ (lmb1) 1.478 0.050 29.760 0.000
m01_02_ (lmb2) -0.453 0.053 -8.581 0.000
m01_03_ (lmb3) -0.312 0.055 -5.697 0.000
m01_04_ (lmb4) 1.586 0.048 33.354 0.000
m01_05_ (lmb5) 1.442 0.049 29.455 0.000
m01_06_ (lmb6) 1.527 0.050 30.720 0.000
m01_07_ (lmb7) -0.284 0.055 -5.202 0.000
m01_08_ (lmb8) -0.444 0.057 -7.749 0.000
m01_09_ (lmb9) -1.460 0.059 -24.872 0.000
m01_10_ (lm10) -0.835 0.058 -14.334 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.ml01_01_1 (i1) 4.156 0.060 68.803 0.000
.ml01_02_1 5.347 0.052 103.093 0.000
.ml01_03_1 5.123 0.053 96.752 0.000
.ml01_04_1 3.973 0.060 65.761 0.000
.ml01_05_1 4.483 0.059 75.615 0.000
.ml01_06_1 3.970 0.061 64.910 0.000
.ml01_07_1 5.086 0.053 96.700 0.000
.ml01_08_1 5.026 0.056 90.118 0.000
.ml01_09_1 3.880 0.067 57.625 0.000
.ml01_10_1 4.885 0.060 82.075 0.000
meaning1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.ml01_01_t1 0.905 0.054 16.872 0.000
.ml01_02_t1 2.073 0.102 20.396 0.000
.ml01_03_t1 2.277 0.111 20.495 0.000
.ml01_04_t1 0.576 0.042 13.801 0.000
.ml01_05_t1 0.898 0.053 16.956 0.000
.ml01_06_t1 0.836 0.052 16.215 0.000
.ml01_07_t1 2.263 0.110 20.510 0.000
.ml01_08_t1 2.438 0.119 20.416 0.000
.ml01_09_t1 1.706 0.091 18.665 0.000
.ml01_10_t1 2.304 0.115 20.001 0.000
meaning1 1.000 tidy(fit_cfa_meaning) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 10 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 meaning1 =~ ml01_04_t1 =~ lambda4 1.59 0.0475 0.902
2 meaning1 =~ ml01_06_t1 =~ lambda6 1.53 0.0497 0.858
3 meaning1 =~ ml01_01_t1 =~ lambda1 1.48 0.0497 0.841
4 meaning1 =~ ml01_09_t1 =~ lambda9 -1.46 0.0587 -0.745
5 meaning1 =~ ml01_05_t1 =~ lambda5 1.44 0.0489 0.836
6 meaning1 =~ ml01_10_t1 =~ lambda10 -0.835 0.0583 -0.482
7 meaning1 =~ ml01_02_t1 =~ lambda2 -0.453 0.0528 -0.300
8 meaning1 =~ ml01_08_t1 =~ lambda8 -0.444 0.0573 -0.273
9 meaning1 =~ ml01_03_t1 =~ lambda3 -0.312 0.0548 -0.203
10 meaning1 =~ ml01_07_t1 =~ lambda7 -0.284 0.0545 -0.185Reverse-code and form parcels:
df_sbsa2_wide_wb <- df_sbsa2_wide_wb %>%
mutate(ml01_09_t1_r = ml01_09_t1,
ml01_10_t1_r = ml01_10_t1,
ml01_02_t1_r = ml01_02_t1,
ml01_08_t1_r = ml01_08_t1,
ml01_07_t1_r = ml01_07_t1,
ml01_03_t1_r = ml01_03_t1,
ml01_09_t2_r = ml01_09_t2,
ml01_10_t2_r = ml01_10_t2,
ml01_02_t2_r = ml01_02_t2,
ml01_08_t2_r = ml01_08_t2,
ml01_07_t2_r = ml01_07_t2,
ml01_03_t2_r = ml01_03_t2,
ml01_09_t3_r = ml01_09_t3,
ml01_10_t3_r = ml01_10_t3,
ml01_02_t3_r = ml01_02_t3,
ml01_08_t3_r = ml01_08_t3,
ml01_07_t3_r = ml01_07_t3,
ml01_03_t3_r = ml01_03_t3) %>%
mutate(across(intersect(starts_with("ml01"), ends_with("_r")),
~ recode(.x, `1` = 7L, `2` = 6L, `3` = 5L, `4` = 4L, `5` = 3L, `6` = 2L, `7` = 1L, .default = NA_integer_))) %>%
mutate(meaning_par1_t1 = rowMeans(across(c(ml01_04_t1, ml01_10_t1_r, ml01_03_t1_r, ml01_07_t1_r)), na.rm=T),
meaning_par2_t1 = rowMeans(across(c(ml01_06_t1, ml01_05_t1, ml01_08_t1_r)), na.rm=T),
meaning_par3_t1 = rowMeans(across(c(ml01_01_t1, ml01_09_t1_r, ml01_02_t1_r)), na.rm=T),
meaning_par1_t2 = rowMeans(across(c(ml01_04_t2, ml01_10_t2_r, ml01_03_t2_r, ml01_07_t2_r)), na.rm=T),
meaning_par2_t2 = rowMeans(across(c(ml01_06_t2, ml01_05_t2, ml01_08_t2_r)), na.rm=T),
meaning_par3_t2 = rowMeans(across(c(ml01_01_t2, ml01_09_t2_r, ml01_02_t2_r)), na.rm=T),
meaning_par1_t3 = rowMeans(across(c(ml01_04_t3, ml01_10_t3_r, ml01_03_t3_r, ml01_07_t3_r)), na.rm=T),
meaning_par2_t3 = rowMeans(across(c(ml01_06_t3, ml01_05_t3, ml01_08_t3_r)), na.rm=T),
meaning_par3_t3 = rowMeans(across(c(ml01_01_t3, ml01_09_t3_r, ml01_02_t3_r)), na.rm=T))Check CFA item loadings at T1
cfa_selfes <- '
# Define the latent factors
selfes1 =~ NA*rs01_01_t1 + lambda1*rs01_01_t1 + lambda2*rs01_02_t1 + lambda3*rs01_03_t1 + lambda4*rs01_04_t1 + lambda5*rs01_05_t1 + lambda6*rs01_06_t1 + lambda7*rs01_07_t1 + lambda8*rs01_08_t1 + lambda9*rs01_09_t1 + lambda10*rs01_10_t1
# Intercepts
rs01_01_t1 ~ i1*1
rs01_02_t1 ~ 1
rs01_03_t1 ~ 1
rs01_04_t1 ~ 1
rs01_05_t1 ~ 1
rs01_06_t1 ~ 1
rs01_07_t1 ~ 1
rs01_08_t1 ~ 1
rs01_09_t1 ~ 1
rs01_10_t1 ~ 1
# Unique Variances
rs01_01_t1 ~~ rs01_01_t1
rs01_02_t1 ~~ rs01_02_t1
rs01_03_t1 ~~ rs01_03_t1
rs01_04_t1 ~~ rs01_04_t1
rs01_05_t1 ~~ rs01_05_t1
rs01_06_t1 ~~ rs01_06_t1
rs01_07_t1 ~~ rs01_07_t1
rs01_08_t1 ~~ rs01_08_t1
rs01_09_t1 ~~ rs01_09_t1
rs01_10_t1 ~~ rs01_10_t1
# Latent Variable Means
selfes1 ~ 0*1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
'
fit_cfa_selfes <- cfa(cfa_selfes, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_cfa_selfes, fit.measures = TRUE)lavaan 0.6.15 ended normally after 15 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 30
Used Total
Number of observations 847 849
Number of missing patterns 1
Model Test User Model:
Test statistic 493.618
Degrees of freedom 35
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4386.991
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.894
Tucker-Lewis Index (TLI) 0.864
Robust Comparative Fit Index (CFI) 0.894
Robust Tucker-Lewis Index (TLI) 0.864
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11502.923
Loglikelihood unrestricted model (H1) -11256.114
Akaike (AIC) 23065.846
Bayesian (BIC) 23208.097
Sample-size adjusted Bayesian (SABIC) 23112.827
Root Mean Square Error of Approximation:
RMSEA 0.124
90 Percent confidence interval - lower 0.115
90 Percent confidence interval - upper 0.134
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.124
90 Percent confidence interval - lower 0.115
90 Percent confidence interval - upper 0.134
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.062
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
selfes1 =~
r01_01_ (lmb1) 0.867 0.036 23.906 0.000
r01_02_ (lmb2) -0.997 0.042 -23.899 0.000
r01_03_ (lmb3) 0.501 0.028 17.737 0.000
r01_04_ (lmb4) 0.571 0.033 17.507 0.000
r01_05_ (lmb5) -0.932 0.039 -23.875 0.000
r01_06_ (lmb6) -1.063 0.041 -26.027 0.000
r01_07_ (lmb7) 0.702 0.033 20.966 0.000
r01_08_ (lmb8) -0.619 0.041 -14.943 0.000
r01_09_ (lmb9) -1.150 0.039 -29.187 0.000
r01_10_ (lm10) 0.979 0.035 27.592 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.rs01_01_1 (i1) 3.034 0.041 74.189 0.000
.rs01_02_1 3.174 0.047 67.592 0.000
.rs01_03_1 3.947 0.030 132.913 0.000
.rs01_04_1 3.824 0.034 111.837 0.000
.rs01_05_1 2.789 0.044 63.327 0.000
.rs01_06_1 3.223 0.047 68.318 0.000
.rs01_07_1 3.648 0.036 100.087 0.000
.rs01_08_1 3.816 0.042 89.803 0.000
.rs01_09_1 2.653 0.047 56.001 0.000
.rs01_10_1 3.218 0.042 76.962 0.000
selfes1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.rs01_01_t1 0.665 0.036 18.392 0.000
.rs01_02_t1 0.873 0.048 18.258 0.000
.rs01_03_t1 0.496 0.025 19.518 0.000
.rs01_04_t1 0.665 0.034 19.560 0.000
.rs01_05_t1 0.774 0.042 18.468 0.000
.rs01_06_t1 0.755 0.043 17.571 0.000
.rs01_07_t1 0.632 0.033 18.965 0.000
.rs01_08_t1 1.146 0.058 19.925 0.000
.rs01_09_t1 0.577 0.036 16.228 0.000
.rs01_10_t1 0.523 0.031 17.066 0.000
selfes1 1.000 tidy(fit_cfa_selfes) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 10 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 selfes1 =~ rs01_09_t1 =~ lambda9 -1.15 0.0394 -0.834
2 selfes1 =~ rs01_06_t1 =~ lambda6 -1.06 0.0409 -0.774
3 selfes1 =~ rs01_02_t1 =~ lambda2 -0.997 0.0417 -0.730
4 selfes1 =~ rs01_10_t1 =~ lambda10 0.979 0.0355 0.804
5 selfes1 =~ rs01_05_t1 =~ lambda5 -0.932 0.0390 -0.727
6 selfes1 =~ rs01_01_t1 =~ lambda1 0.867 0.0363 0.729
7 selfes1 =~ rs01_07_t1 =~ lambda7 0.702 0.0335 0.662
8 selfes1 =~ rs01_08_t1 =~ lambda8 -0.619 0.0414 -0.500
9 selfes1 =~ rs01_04_t1 =~ lambda4 0.571 0.0326 0.573
10 selfes1 =~ rs01_03_t1 =~ lambda3 0.501 0.0283 0.580Reverse-code and form parcels:
df_sbsa2_wide_wb <- df_sbsa2_wide_wb %>%
mutate(rs01_09_t1_r = rs01_09_t1,
rs01_06_t1_r = rs01_06_t1,
rs01_02_t1_r = rs01_02_t1,
rs01_05_t1_r = rs01_05_t1,
rs01_08_t1_r = rs01_08_t1,
rs01_09_t2_r = rs01_09_t2,
rs01_06_t2_r = rs01_06_t2,
rs01_02_t2_r = rs01_02_t2,
rs01_05_t2_r = rs01_05_t2,
rs01_08_t2_r = rs01_08_t2,
rs01_09_t3_r = rs01_09_t3,
rs01_06_t3_r = rs01_06_t3,
rs01_02_t3_r = rs01_02_t3,
rs01_05_t3_r = rs01_05_t3,
rs01_08_t3_r = rs01_08_t3) %>%
mutate(across(intersect(starts_with("rs01"), ends_with("_r")),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(selfes_par1_t1 = rowMeans(across(c(rs01_09_t1_r, rs01_01_t1, rs01_04_t1, rs01_03_t1)), na.rm=T),
selfes_par2_t1 = rowMeans(across(c(rs01_06_t1_r, rs01_05_t1_r, rs01_08_t1_r)), na.rm=T),
selfes_par3_t1 = rowMeans(across(c(rs01_02_t1_r, rs01_10_t1, rs01_07_t1)), na.rm=T),
selfes_par1_t2 = rowMeans(across(c(rs01_09_t2_r, rs01_01_t2, rs01_04_t2, rs01_03_t2)), na.rm=T),
selfes_par2_t2 = rowMeans(across(c(rs01_06_t2_r, rs01_05_t2_r, rs01_08_t2_r)), na.rm=T),
selfes_par3_t2 = rowMeans(across(c(rs01_02_t2_r, rs01_10_t2, rs01_07_t2)), na.rm=T),
selfes_par1_t3 = rowMeans(across(c(rs01_09_t3_r, rs01_01_t3, rs01_04_t3, rs01_03_t3)), na.rm=T),
selfes_par2_t3 = rowMeans(across(c(rs01_06_t3_r, rs01_05_t3_r, rs01_08_t3_r)), na.rm=T),
selfes_par3_t3 = rowMeans(across(c(rs01_02_t3_r, rs01_10_t3, rs01_07_t3)), na.rm=T))Check CFA item loadings at T1
cfa_concept <- '
# Define the latent factors
concept1 =~ NA*sc01_01_t1 + lambda1*sc01_01_t1 + lambda2*sc01_02_t1 + lambda3*sc01_03_t1 + lambda4*sc01_04_t1 + lambda5*sc01_05_t1 + lambda6*sc01_06_t1 + lambda7*sc01_07_t1 + lambda8*sc01_08_t1 + lambda9*sc01_09_t1 + lambda10*sc01_10_t1 + lambda11*sc01_11_t1 + lambda12*sc01_12_t1
# Intercepts
sc01_01_t1 ~ i1*1
sc01_02_t1 ~ 1
sc01_03_t1 ~ 1
sc01_04_t1 ~ 1
sc01_05_t1 ~ 1
sc01_06_t1 ~ 1
sc01_07_t1 ~ 1
sc01_08_t1 ~ 1
sc01_09_t1 ~ 1
sc01_10_t1 ~ 1
sc01_11_t1 ~ 1
sc01_12_t1 ~ 1
# Unique Variances
sc01_01_t1 ~~ sc01_01_t1
sc01_02_t1 ~~ sc01_02_t1
sc01_03_t1 ~~ sc01_03_t1
sc01_04_t1 ~~ sc01_04_t1
sc01_05_t1 ~~ sc01_05_t1
sc01_06_t1 ~~ sc01_06_t1
sc01_07_t1 ~~ sc01_07_t1
sc01_08_t1 ~~ sc01_08_t1
sc01_09_t1 ~~ sc01_09_t1
sc01_10_t1 ~~ sc01_10_t1
sc01_11_t1 ~~ sc01_11_t1
sc01_12_t1 ~~ sc01_12_t1
# Latent Variable Means
concept1 ~ 0*1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
'
fit_cfa_concept <- cfa(cfa_concept, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_cfa_concept, fit.measures = TRUE)lavaan 0.6.15 ended normally after 32 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 847 849
Number of missing patterns 2
Model Test User Model:
Test statistic 430.284
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4809.241
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.921
Tucker-Lewis Index (TLI) 0.903
Robust Comparative Fit Index (CFI) 0.921
Robust Tucker-Lewis Index (TLI) 0.903
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -15026.110
Loglikelihood unrestricted model (H1) -14810.968
Akaike (AIC) 30124.220
Bayesian (BIC) 30294.921
Sample-size adjusted Bayesian (SABIC) 30180.596
Root Mean Square Error of Approximation:
RMSEA 0.091
90 Percent confidence interval - lower 0.083
90 Percent confidence interval - upper 0.099
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.987
Robust RMSEA 0.091
90 Percent confidence interval - lower 0.083
90 Percent confidence interval - upper 0.099
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.987
Standardized Root Mean Square Residual:
SRMR 0.040
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
concept1 =~
s01_01_ (lmb1) 0.939 0.040 23.441 0.000
s01_02_ (lmb2) 1.028 0.042 24.709 0.000
s01_03_ (lmb3) 0.965 0.041 23.263 0.000
s01_04_ (lmb4) 1.030 0.041 25.217 0.000
s01_05_ (lmb5) 0.820 0.043 18.976 0.000
s01_06_ (lmb6) 0.038 0.044 0.874 0.382
s01_07_ (lmb7) 0.801 0.044 18.176 0.000
s01_08_ (lmb8) 1.053 0.038 28.033 0.000
s01_09_ (lmb9) 1.034 0.039 26.382 0.000
s01_10_ (lm10) 0.934 0.040 23.116 0.000
s01_11_ (lm11) -0.760 0.039 -19.544 0.000
s01_12_ (lm12) 0.841 0.043 19.695 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.sc01_01_1 (i1) 3.157 0.045 70.268 0.000
.sc01_02_1 3.378 0.047 71.322 0.000
.sc01_03_1 3.232 0.046 69.556 0.000
.sc01_04_1 3.139 0.047 67.116 0.000
.sc01_05_1 2.974 0.046 64.411 0.000
.sc01_06_1 2.889 0.042 69.113 0.000
.sc01_07_1 2.700 0.047 57.795 0.000
.sc01_08_1 2.753 0.045 61.854 0.000
.sc01_09_1 2.856 0.046 62.701 0.000
.sc01_10_1 2.926 0.045 64.757 0.000
.sc01_11_1 3.243 0.042 77.576 0.000
.sc01_12_1 3.115 0.046 67.710 0.000
concept1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.sc01_01_t1 0.829 0.045 18.572 0.000
.sc01_02_t1 0.843 0.046 18.184 0.000
.sc01_03_t1 0.896 0.048 18.622 0.000
.sc01_04_t1 0.793 0.044 18.037 0.000
.sc01_05_t1 1.134 0.058 19.475 0.000
.sc01_06_t1 1.479 0.072 20.577 0.000
.sc01_07_t1 1.208 0.062 19.612 0.000
.sc01_08_t1 0.570 0.034 16.947 0.000
.sc01_09_t1 0.688 0.039 17.655 0.000
.sc01_10_t1 0.856 0.046 18.662 0.000
.sc01_11_t1 0.903 0.047 19.403 0.000
.sc01_12_t1 1.085 0.056 19.402 0.000
concept1 1.000 tidy(fit_cfa_concept) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 concept1 =~ sc01_08_t1 =~ lambda8 1.05 0.0375 0.812
2 concept1 =~ sc01_09_t1 =~ lambda9 1.03 0.0392 0.780
3 concept1 =~ sc01_04_t1 =~ lambda4 1.03 0.0408 0.756
4 concept1 =~ sc01_02_t1 =~ lambda2 1.03 0.0416 0.746
5 concept1 =~ sc01_03_t1 =~ lambda3 0.965 0.0415 0.714
6 concept1 =~ sc01_01_t1 =~ lambda1 0.939 0.0400 0.718
7 concept1 =~ sc01_10_t1 =~ lambda10 0.934 0.0404 0.710
8 concept1 =~ sc01_12_t1 =~ lambda12 0.841 0.0427 0.628
9 concept1 =~ sc01_05_t1 =~ lambda5 0.820 0.0432 0.610
10 concept1 =~ sc01_07_t1 =~ lambda7 0.801 0.0440 0.589
11 concept1 =~ sc01_11_t1 =~ lambda11 -0.760 0.0389 -0.624
12 concept1 =~ sc01_06_t1 =~ lambda6 0.0381 0.0436 0.0313Reverse-code and form parcels:
df_sbsa2_wide_wb <- df_sbsa2_wide_wb %>% # based on the item content, it makes more sense here to recode all except sc01_11
mutate(sc01_01_t1_r = sc01_01_t1,
sc01_02_t1_r = sc01_02_t1,
sc01_03_t1_r = sc01_03_t1,
sc01_04_t1_r = sc01_04_t1,
sc01_05_t1_r = sc01_05_t1,
sc01_06_t1_r = sc01_06_t1,
sc01_07_t1_r = sc01_07_t1,
sc01_08_t1_r = sc01_08_t1,
sc01_09_t1_r = sc01_09_t1,
sc01_10_t1_r = sc01_10_t1,
sc01_12_t1_r = sc01_12_t1,
sc01_01_t2_r = sc01_01_t2,
sc01_02_t2_r = sc01_02_t2,
sc01_03_t2_r = sc01_03_t2,
sc01_04_t2_r = sc01_04_t2,
sc01_05_t2_r = sc01_05_t2,
sc01_06_t2_r = sc01_06_t2,
sc01_07_t2_r = sc01_07_t2,
sc01_08_t2_r = sc01_08_t2,
sc01_09_t2_r = sc01_09_t2,
sc01_10_t2_r = sc01_10_t2,
sc01_12_t2_r = sc01_12_t2,
sc01_01_t3_r = sc01_01_t3,
sc01_02_t3_r = sc01_02_t3,
sc01_03_t3_r = sc01_03_t3,
sc01_04_t3_r = sc01_04_t3,
sc01_05_t3_r = sc01_05_t3,
sc01_06_t3_r = sc01_06_t3,
sc01_07_t3_r = sc01_07_t3,
sc01_08_t3_r = sc01_08_t3,
sc01_09_t3_r = sc01_09_t3,
sc01_10_t3_r = sc01_10_t3,
sc01_12_t3_r = sc01_12_t3) %>%
mutate(across(intersect(starts_with("sc01"), ends_with("_r")),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(concept_par1_t1 = rowMeans(across(c(sc01_08_t1_r, sc01_01_t1_r, sc01_05_t1_r, sc01_06_t1_r)), na.rm=T),
concept_par2_t1 = rowMeans(across(c(sc01_09_t1_r, sc01_03_t1_r, sc01_12_t1_r, sc01_11_t1)), na.rm=T),
concept_par3_t1 = rowMeans(across(c(sc01_04_t1_r, sc01_02_t1_r, sc01_10_t1_r, sc01_07_t1_r)), na.rm=T),
concept_par1_t2 = rowMeans(across(c(sc01_08_t2_r, sc01_01_t2_r, sc01_05_t2_r, sc01_06_t2_r)), na.rm=T),
concept_par2_t2 = rowMeans(across(c(sc01_09_t2_r, sc01_03_t2_r, sc01_12_t2_r, sc01_11_t2)), na.rm=T),
concept_par3_t2 = rowMeans(across(c(sc01_04_t2_r, sc01_02_t2_r, sc01_10_t2_r, sc01_07_t2_r)), na.rm=T),
concept_par1_t3 = rowMeans(across(c(sc01_08_t3_r, sc01_01_t3_r, sc01_05_t3_r, sc01_06_t3_r)), na.rm=T),
concept_par2_t3 = rowMeans(across(c(sc01_09_t3_r, sc01_03_t3_r, sc01_12_t3_r, sc01_11_t3)), na.rm=T),
concept_par3_t3 = rowMeans(across(c(sc01_04_t3_r, sc01_02_t3_r, sc01_10_t3_r, sc01_07_t3_r)), na.rm=T))# all time points
df_sbsa2_wide_pers <- df_sbsa2 %>%
arrange(pid, time) %>%
select(pid, rando, time, starts_with(c("bf05", "bf06"))) %>%
mutate(valid=1) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(valid, starts_with(c("bf05", "bf06"))))
# colnames(df_sbsa2_wide_pers)Check CFA item loadings at T1
cfa_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*bf05_01_t1 + lambda1*bf05_01_t1 + lambda2*bf05_06_t1 + lambda3*bf05_11_t1 + lambda4*bf05_16_t1 + lambda5*bf05_21_t1 + lambda6*bf05_26_t1 + lambda7*bf05_31_t1 + lambda8*bf05_36_t1 + lambda9*bf05_41_t1 + lambda10*bf05_46_t1 + lambda11*bf05_51_t1 + lambda12*bf05_56_t1
# Intercepts
bf05_01_t1 ~ i1*1
bf05_06_t1 ~ 1
bf05_11_t1 ~ 1
bf05_16_t1 ~ 1
bf05_21_t1 ~ 1
bf05_26_t1 ~ 1
bf05_31_t1 ~ 1
bf05_36_t1 ~ 1
bf05_41_t1 ~ 1
bf05_46_t1 ~ 1
bf05_51_t1 ~ 1
bf05_56_t1 ~ 1
# Unique Variances
bf05_01_t1 ~~ bf05_01_t1
bf05_06_t1 ~~ bf05_06_t1
bf05_11_t1 ~~ bf05_11_t1
bf05_16_t1 ~~ bf05_16_t1
bf05_21_t1 ~~ bf05_21_t1
bf05_26_t1 ~~ bf05_26_t1
bf05_31_t1 ~~ bf05_31_t1
bf05_36_t1 ~~ bf05_36_t1
bf05_41_t1 ~~ bf05_41_t1
bf05_46_t1 ~~ bf05_46_t1
bf05_51_t1 ~~ bf05_51_t1
bf05_56_t1 ~~ bf05_56_t1
# Latent Variable Means
extra_curr1 ~ 0*1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
'
fit_cfa_extra_curr <- cfa(cfa_extra_curr, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_extra_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 5
Model Test User Model:
Test statistic 847.969
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3167.484
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.744
Tucker-Lewis Index (TLI) 0.687
Robust Comparative Fit Index (CFI) 0.744
Robust Tucker-Lewis Index (TLI) 0.687
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -15300.920
Loglikelihood unrestricted model (H1) -14876.935
Akaike (AIC) 30673.840
Bayesian (BIC) 30844.584
Sample-size adjusted Bayesian (SABIC) 30730.259
Root Mean Square Error of Approximation:
RMSEA 0.132
90 Percent confidence interval - lower 0.124
90 Percent confidence interval - upper 0.140
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.132
90 Percent confidence interval - lower 0.124
90 Percent confidence interval - upper 0.140
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.079
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
extra_curr1 =~
b05_01_ (lmb1) 0.945 0.042 22.295 0.000
b05_06_ (lmb2) 0.540 0.042 12.805 0.000
b05_11_ (lmb3) -0.322 0.044 -7.334 0.000
b05_16_ (lmb4) -0.822 0.041 -19.905 0.000
b05_21_ (lmb5) 0.770 0.043 18.089 0.000
b05_26_ (lmb6) -0.542 0.047 -11.619 0.000
b05_31_ (lmb7) -0.776 0.036 -21.343 0.000
b05_36_ (lmb8) -0.468 0.041 -11.547 0.000
b05_41_ (lmb9) 0.676 0.041 16.507 0.000
b05_46_ (lm10) 0.925 0.043 21.606 0.000
b05_51_ (lm11) -0.586 0.043 -13.508 0.000
b05_56_ (lm12) 0.600 0.039 15.397 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_01_1 (i1) 2.759 0.046 60.440 0.000
.bf05_06_1 3.073 0.041 75.058 0.000
.bf05_11_1 2.769 0.041 66.976 0.000
.bf05_16_1 3.755 0.043 87.599 0.000
.bf05_21_1 2.721 0.043 62.705 0.000
.bf05_26_1 3.057 0.045 68.086 0.000
.bf05_31_1 4.001 0.039 103.823 0.000
.bf05_36_1 3.158 0.039 80.714 0.000
.bf05_41_1 2.862 0.041 69.918 0.000
.bf05_46_1 2.919 0.046 63.955 0.000
.bf05_51_1 3.188 0.042 75.107 0.000
.bf05_56_1 3.279 0.039 84.689 0.000
extr_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_01_t1 0.874 0.052 16.939 0.000
.bf05_06_t1 1.130 0.058 19.592 0.000
.bf05_11_t1 1.343 0.066 20.296 0.000
.bf05_16_t1 0.881 0.050 17.454 0.000
.bf05_21_t1 1.003 0.055 18.195 0.000
.bf05_26_t1 1.415 0.072 19.774 0.000
.bf05_31_t1 0.656 0.038 17.105 0.000
.bf05_36_t1 1.079 0.054 19.840 0.000
.bf05_41_t1 0.961 0.052 18.580 0.000
.bf05_46_t1 0.909 0.053 17.105 0.000
.bf05_51_t1 1.184 0.061 19.467 0.000
.bf05_56_t1 0.910 0.048 19.012 0.000
extra_curr1 1.000 tidy(fit_cfa_extra_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 extra_curr1 =~ bf05_01_t1 =~ lambda1 0.945 0.0424 0.711
2 extra_curr1 =~ bf05_46_t1 =~ lambda10 0.925 0.0428 0.696
3 extra_curr1 =~ bf05_16_t1 =~ lambda4 -0.822 0.0413 -0.659
4 extra_curr1 =~ bf05_31_t1 =~ lambda7 -0.776 0.0364 -0.692
5 extra_curr1 =~ bf05_21_t1 =~ lambda5 0.770 0.0426 0.610
6 extra_curr1 =~ bf05_41_t1 =~ lambda9 0.676 0.0410 0.568
7 extra_curr1 =~ bf05_56_t1 =~ lambda12 0.600 0.0389 0.532
8 extra_curr1 =~ bf05_51_t1 =~ lambda11 -0.586 0.0434 -0.474
9 extra_curr1 =~ bf05_26_t1 =~ lambda6 -0.542 0.0467 -0.415
10 extra_curr1 =~ bf05_06_t1 =~ lambda2 0.540 0.0421 0.453
11 extra_curr1 =~ bf05_36_t1 =~ lambda8 -0.468 0.0405 -0.411
12 extra_curr1 =~ bf05_11_t1 =~ lambda3 -0.322 0.0438 -0.267Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf05_31_t1_r = bf05_31_t1,
bf05_16_t1_r = bf05_16_t1,
bf05_26_t1_r = bf05_26_t1,
bf05_51_t1_r = bf05_51_t1,
bf05_36_t1_r = bf05_36_t1,
bf05_11_t1_r = bf05_11_t1,
bf05_31_t2_r = bf05_31_t2,
bf05_16_t2_r = bf05_16_t2,
bf05_26_t2_r = bf05_26_t2,
bf05_51_t2_r = bf05_51_t2,
bf05_36_t2_r = bf05_36_t2,
bf05_11_t2_r = bf05_11_t2,
bf05_31_t3_r = bf05_31_t3,
bf05_16_t3_r = bf05_16_t3,
bf05_26_t3_r = bf05_26_t3,
bf05_51_t3_r = bf05_51_t3,
bf05_36_t3_r = bf05_36_t3,
bf05_11_t3_r = bf05_11_t3) %>%
mutate(across(c(bf05_31_t1_r, bf05_16_t1_r, bf05_26_t1_r, bf05_51_t1_r, bf05_36_t1_r, bf05_11_t1_r,
bf05_31_t2_r, bf05_16_t2_r, bf05_26_t2_r, bf05_51_t2_r, bf05_36_t2_r, bf05_11_t2_r,
bf05_31_t3_r, bf05_16_t3_r, bf05_26_t3_r, bf05_51_t3_r, bf05_36_t3_r, bf05_11_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(extra_curr_par1_t1 = rowMeans(across(c(bf05_01_t1, bf05_41_t1, bf05_26_t1_r, bf05_11_t1_r)), na.rm=T),
extra_curr_par2_t1 = rowMeans(across(c(bf05_46_t1, bf05_21_t1, bf05_51_t1_r, bf05_36_t1_r)), na.rm=T),
extra_curr_par3_t1 = rowMeans(across(c(bf05_16_t1_r, bf05_31_t1_r, bf05_56_t1, bf05_06_t1)), na.rm=T),
extra_curr_par1_t2 = rowMeans(across(c(bf05_01_t2, bf05_41_t2, bf05_26_t2_r, bf05_11_t2_r)), na.rm=T),
extra_curr_par2_t2 = rowMeans(across(c(bf05_46_t2, bf05_21_t2, bf05_51_t2_r, bf05_36_t2_r)), na.rm=T),
extra_curr_par3_t2 = rowMeans(across(c(bf05_16_t2_r, bf05_31_t2_r, bf05_56_t2, bf05_06_t2)), na.rm=T),
extra_curr_par1_t3 = rowMeans(across(c(bf05_01_t3, bf05_41_t3, bf05_26_t3_r, bf05_11_t3_r)), na.rm=T),
extra_curr_par2_t3 = rowMeans(across(c(bf05_46_t3, bf05_21_t3, bf05_51_t3_r, bf05_36_t3_r)), na.rm=T),
extra_curr_par3_t3 = rowMeans(across(c(bf05_16_t3_r, bf05_31_t3_r, bf05_56_t3, bf05_06_t3)), na.rm=T))Check CFA item loadings at T1
cfa_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*bf06_01_t1 + lambda1*bf06_01_t1 + lambda2*bf06_06_t1 + lambda3*bf06_11_t1 + lambda4*bf06_16_t1 + lambda5*bf06_21_t1 + lambda6*bf06_26_t1 + lambda7*bf06_31_t1 + lambda8*bf06_36_t1 + lambda9*bf06_41_t1 + lambda10*bf06_46_t1 + lambda11*bf06_51_t1 + lambda12*bf06_56_t1
# Intercepts
bf06_01_t1 ~ i1*1
bf06_06_t1 ~ 1
bf06_11_t1 ~ 1
bf06_16_t1 ~ 1
bf06_21_t1 ~ 1
bf06_26_t1 ~ 1
bf06_31_t1 ~ 1
bf06_36_t1 ~ 1
bf06_41_t1 ~ 1
bf06_46_t1 ~ 1
bf06_51_t1 ~ 1
bf06_56_t1 ~ 1
# Unique Variances
bf06_01_t1 ~~ bf06_01_t1
bf06_06_t1 ~~ bf06_06_t1
bf06_11_t1 ~~ bf06_11_t1
bf06_16_t1 ~~ bf06_16_t1
bf06_21_t1 ~~ bf06_21_t1
bf06_26_t1 ~~ bf06_26_t1
bf06_31_t1 ~~ bf06_31_t1
bf06_36_t1 ~~ bf06_36_t1
bf06_41_t1 ~~ bf06_41_t1
bf06_46_t1 ~~ bf06_46_t1
bf06_51_t1 ~~ bf06_51_t1
bf06_56_t1 ~~ bf06_56_t1
# Latent Variable Means
extra_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
'
fit_cfa_extra_ideal <- cfa(cfa_extra_ideal, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_extra_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 40 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 6
Model Test User Model:
Test statistic 254.727
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1146.934
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.814
Tucker-Lewis Index (TLI) 0.773
Robust Comparative Fit Index (CFI) 0.816
Robust Tucker-Lewis Index (TLI) 0.775
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -14072.893
Loglikelihood unrestricted model (H1) -13945.529
Akaike (AIC) 28217.786
Bayesian (BIC) 28388.529
Sample-size adjusted Bayesian (SABIC) 28274.204
Root Mean Square Error of Approximation:
RMSEA 0.066
90 Percent confidence interval - lower 0.058
90 Percent confidence interval - upper 0.075
P-value H_0: RMSEA <= 0.050 0.001
P-value H_0: RMSEA >= 0.080 0.003
Robust RMSEA 0.066
90 Percent confidence interval - lower 0.058
90 Percent confidence interval - upper 0.075
P-value H_0: Robust RMSEA <= 0.050 0.001
P-value H_0: Robust RMSEA >= 0.080 0.003
Standardized Root Mean Square Residual:
SRMR 0.049
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
extra_ideal1 =~
b06_01_ (lmb1) 0.409 0.034 12.191 0.000
b06_06_ (lmb2) 0.250 0.036 6.881 0.000
b06_11_ (lmb3) -0.307 0.054 -5.691 0.000
b06_16_ (lmb4) -0.423 0.049 -8.670 0.000
b06_21_ (lmb5) 0.478 0.042 11.421 0.000
b06_26_ (lmb6) -0.430 0.039 -10.915 0.000
b06_31_ (lmb7) -0.491 0.042 -11.702 0.000
b06_36_ (lmb8) -0.411 0.044 -9.303 0.000
b06_41_ (lmb9) 0.383 0.029 13.350 0.000
b06_46_ (lm10) 0.390 0.040 9.738 0.000
b06_51_ (lm11) -0.485 0.042 -11.413 0.000
b06_56_ (lm12) 0.466 0.033 14.281 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_01_1 (i1) 4.237 0.030 142.383 0.000
.bf06_06_1 4.128 0.031 132.290 0.000
.bf06_11_1 2.257 0.046 48.598 0.000
.bf06_16_1 2.857 0.042 68.327 0.000
.bf06_21_1 3.803 0.037 102.781 0.000
.bf06_26_1 1.722 0.035 49.589 0.000
.bf06_31_1 2.184 0.037 59.386 0.000
.bf06_36_1 2.201 0.038 57.183 0.000
.bf06_41_1 4.471 0.025 176.151 0.000
.bf06_46_1 3.713 0.035 105.946 0.000
.bf06_51_1 2.392 0.037 64.098 0.000
.bf06_56_1 4.265 0.029 146.413 0.000
extra_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_01_t1 0.580 0.032 18.206 0.000
.bf06_06_t1 0.760 0.038 19.886 0.000
.bf06_11_t1 1.733 0.086 20.146 0.000
.bf06_16_t1 1.301 0.067 19.369 0.000
.bf06_21_t1 0.930 0.050 18.530 0.000
.bf06_26_t1 0.834 0.044 18.786 0.000
.bf06_31_t1 0.901 0.049 18.265 0.000
.bf06_36_t1 1.083 0.056 19.300 0.000
.bf06_41_t1 0.399 0.023 17.422 0.000
.bf06_46_t1 0.887 0.046 19.161 0.000
.bf06_51_t1 0.942 0.051 18.479 0.000
.bf06_56_t1 0.502 0.030 16.791 0.000
extra_ideal1 1.000 tidy(fit_cfa_extra_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 extra_ideal1 =~ bf06_31_t1 =~ lambda7 -0.491 0.0419 -0.459
2 extra_ideal1 =~ bf06_51_t1 =~ lambda11 -0.485 0.0425 -0.447
3 extra_ideal1 =~ bf06_21_t1 =~ lambda5 0.478 0.0418 0.444
4 extra_ideal1 =~ bf06_56_t1 =~ lambda12 0.466 0.0326 0.549
5 extra_ideal1 =~ bf06_26_t1 =~ lambda6 -0.430 0.0394 -0.426
6 extra_ideal1 =~ bf06_16_t1 =~ lambda4 -0.423 0.0488 -0.348
7 extra_ideal1 =~ bf06_36_t1 =~ lambda8 -0.411 0.0442 -0.367
8 extra_ideal1 =~ bf06_01_t1 =~ lambda1 0.409 0.0336 0.473
9 extra_ideal1 =~ bf06_46_t1 =~ lambda10 0.390 0.0400 0.382
10 extra_ideal1 =~ bf06_41_t1 =~ lambda9 0.383 0.0287 0.518
11 extra_ideal1 =~ bf06_11_t1 =~ lambda3 -0.307 0.0540 -0.227
12 extra_ideal1 =~ bf06_06_t1 =~ lambda2 0.250 0.0364 0.276Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf06_31_t1_r = bf06_31_t1,
bf06_16_t1_r = bf06_16_t1,
bf06_26_t1_r = bf06_26_t1,
bf06_51_t1_r = bf06_51_t1,
bf06_36_t1_r = bf06_36_t1,
bf06_11_t1_r = bf06_11_t1,
bf06_31_t2_r = bf06_31_t2,
bf06_16_t2_r = bf06_16_t2,
bf06_26_t2_r = bf06_26_t2,
bf06_51_t2_r = bf06_51_t2,
bf06_36_t2_r = bf06_36_t2,
bf06_11_t2_r = bf06_11_t2,
bf06_31_t3_r = bf06_31_t3,
bf06_16_t3_r = bf06_16_t3,
bf06_26_t3_r = bf06_26_t3,
bf06_51_t3_r = bf06_51_t3,
bf06_36_t3_r = bf06_36_t3,
bf06_11_t3_r = bf06_11_t3) %>%
mutate(across(c(bf06_31_t1_r, bf06_16_t1_r, bf06_26_t1_r, bf06_51_t1_r, bf06_36_t1_r, bf06_11_t1_r,
bf06_31_t2_r, bf06_16_t2_r, bf06_26_t2_r, bf06_51_t2_r, bf06_36_t2_r, bf06_11_t2_r,
bf06_31_t3_r, bf06_16_t3_r, bf06_26_t3_r, bf06_51_t3_r, bf06_36_t3_r, bf06_11_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(extra_ideal_par1_t1 = rowMeans(across(c(bf06_31_t1_r, bf06_16_t1_r, bf06_46_t1, bf06_06_t1)), na.rm=T),
extra_ideal_par2_t1 = rowMeans(across(c(bf06_21_t1, bf06_26_t1_r, bf06_01_t1, bf06_11_t1_r)), na.rm=T),
extra_ideal_par3_t1 = rowMeans(across(c(bf06_51_t1_r, bf06_56_t1, bf06_36_t1_r, bf06_41_t1)), na.rm=T),
extra_ideal_par1_t2 = rowMeans(across(c(bf06_31_t2_r, bf06_16_t2_r, bf06_46_t2, bf06_06_t2)), na.rm=T),
extra_ideal_par2_t2 = rowMeans(across(c(bf06_21_t2, bf06_26_t2_r, bf06_01_t2, bf06_11_t2_r)), na.rm=T),
extra_ideal_par3_t2 = rowMeans(across(c(bf06_51_t2_r, bf06_56_t2, bf06_36_t2_r, bf06_41_t2)), na.rm=T),
extra_ideal_par1_t3 = rowMeans(across(c(bf06_31_t3_r, bf06_16_t3_r, bf06_46_t3, bf06_06_t3)), na.rm=T),
extra_ideal_par2_t3 = rowMeans(across(c(bf06_21_t3, bf06_26_t3_r, bf06_01_t3, bf06_11_t3_r)), na.rm=T),
extra_ideal_par3_t3 = rowMeans(across(c(bf06_51_t3_r, bf06_56_t3, bf06_36_t3_r, bf06_41_t3)), na.rm=T))Check CFA item loadings at T1
cfa_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*bf05_02_t1 + lambda1*bf05_02_t1 + lambda2*bf05_07_t1 + lambda3*bf05_12_t1 + lambda4*bf05_17_t1 + lambda5*bf05_22_t1 + lambda6*bf05_27_t1 + lambda7*bf05_32_t1 + lambda8*bf05_37_t1 + lambda9*bf05_42_t1 + lambda10*bf05_47_t1 + lambda11*bf05_52_t1 + lambda12*bf05_57_t1
# Intercepts
bf05_02_t1 ~ i1*1
bf05_07_t1 ~ 1
bf05_12_t1 ~ 1
bf05_17_t1 ~ 1
bf05_22_t1 ~ 1
bf05_27_t1 ~ 1
bf05_32_t1 ~ 1
bf05_37_t1 ~ 1
bf05_42_t1 ~ 1
bf05_47_t1 ~ 1
bf05_52_t1 ~ 1
bf05_57_t1 ~ 1
# Unique Variances
bf05_02_t1 ~~ bf05_02_t1
bf05_07_t1 ~~ bf05_07_t1
bf05_12_t1 ~~ bf05_12_t1
bf05_17_t1 ~~ bf05_17_t1
bf05_22_t1 ~~ bf05_22_t1
bf05_27_t1 ~~ bf05_27_t1
bf05_32_t1 ~~ bf05_32_t1
bf05_37_t1 ~~ bf05_37_t1
bf05_42_t1 ~~ bf05_42_t1
bf05_47_t1 ~~ bf05_47_t1
bf05_52_t1 ~~ bf05_52_t1
bf05_57_t1 ~~ bf05_57_t1
# Latent Variable Means
agree_curr1 ~ 0*1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
'
fit_cfa_agree_curr <- cfa(cfa_agree_curr, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_agree_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 41 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 3
Model Test User Model:
Test statistic 532.762
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2295.706
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.785
Tucker-Lewis Index (TLI) 0.738
Robust Comparative Fit Index (CFI) 0.785
Robust Tucker-Lewis Index (TLI) 0.737
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -14316.892
Loglikelihood unrestricted model (H1) -14050.512
Akaike (AIC) 28705.785
Bayesian (BIC) 28876.529
Sample-size adjusted Bayesian (SABIC) 28762.203
Root Mean Square Error of Approximation:
RMSEA 0.102
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.110
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.102
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.110
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.065
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
agree_curr1 =~
b05_02_ (lmb1) 0.483 0.031 15.345 0.000
b05_07_ (lmb2) 0.430 0.025 17.189 0.000
b05_12_ (lmb3) -0.606 0.044 -13.801 0.000
b05_17_ (lmb4) -0.182 0.055 -3.291 0.001
b05_22_ (lmb5) -0.643 0.043 -15.116 0.000
b05_27_ (lmb6) 0.595 0.042 14.270 0.000
b05_32_ (lmb7) 0.529 0.032 16.781 0.000
b05_37_ (lmb8) -0.741 0.044 -17.020 0.000
b05_42_ (lmb9) -0.419 0.045 -9.406 0.000
b05_47_ (lm10) -0.789 0.044 -17.761 0.000
b05_52_ (lm11) 0.418 0.025 16.841 0.000
b05_57_ (lm12) 0.558 0.043 12.968 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_02_1 (i1) 4.188 0.031 136.262 0.000
.bf05_07_1 4.449 0.025 179.500 0.000
.bf05_12_1 2.877 0.042 68.327 0.000
.bf05_17_1 2.667 0.050 52.975 0.000
.bf05_22_1 2.172 0.041 52.346 0.000
.bf05_27_1 3.598 0.040 89.031 0.000
.bf05_32_1 3.963 0.031 126.684 0.000
.bf05_37_1 2.486 0.043 57.567 0.000
.bf05_42_1 3.590 0.041 87.538 0.000
.bf05_47_1 2.526 0.045 56.750 0.000
.bf05_52_1 4.300 0.025 175.434 0.000
.bf05_57_1 3.201 0.041 78.189 0.000
agre_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_02_t1 0.567 0.031 18.581 0.000
.bf05_07_t1 0.336 0.019 17.849 0.000
.bf05_12_t1 1.137 0.060 18.981 0.000
.bf05_17_t1 2.113 0.103 20.507 0.000
.bf05_22_t1 1.045 0.056 18.699 0.000
.bf05_27_t1 1.029 0.054 18.970 0.000
.bf05_32_t1 0.550 0.030 18.167 0.000
.bf05_37_t1 1.031 0.057 17.986 0.000
.bf05_42_t1 1.250 0.063 19.829 0.000
.bf05_47_t1 1.056 0.059 17.798 0.000
.bf05_52_t1 0.335 0.019 17.988 0.000
.bf05_57_t1 1.109 0.058 19.161 0.000
agree_curr1 1.000 tidy(fit_cfa_agree_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 agree_curr1 =~ bf05_47_t1 =~ lambda10 -0.789 0.0444 -0.609
2 agree_curr1 =~ bf05_37_t1 =~ lambda8 -0.741 0.0435 -0.589
3 agree_curr1 =~ bf05_22_t1 =~ lambda5 -0.643 0.0425 -0.532
4 agree_curr1 =~ bf05_12_t1 =~ lambda3 -0.606 0.0439 -0.494
5 agree_curr1 =~ bf05_27_t1 =~ lambda6 0.595 0.0417 0.506
6 agree_curr1 =~ bf05_57_t1 =~ lambda12 0.558 0.0430 0.468
7 agree_curr1 =~ bf05_32_t1 =~ lambda7 0.529 0.0315 0.581
8 agree_curr1 =~ bf05_02_t1 =~ lambda1 0.483 0.0315 0.540
9 agree_curr1 =~ bf05_07_t1 =~ lambda2 0.430 0.0250 0.595
10 agree_curr1 =~ bf05_42_t1 =~ lambda9 -0.419 0.0446 -0.351
11 agree_curr1 =~ bf05_52_t1 =~ lambda11 0.418 0.0248 0.585
12 agree_curr1 =~ bf05_17_t1 =~ lambda4 -0.182 0.0554 -0.124Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf05_47_t1_r = bf05_47_t1,
bf05_37_t1_r = bf05_37_t1,
bf05_22_t1_r = bf05_22_t1,
bf05_12_t1_r = bf05_12_t1,
bf05_42_t1_r = bf05_42_t1,
bf05_17_t1_r = bf05_17_t1,
bf05_47_t2_r = bf05_47_t2,
bf05_37_t2_r = bf05_37_t2,
bf05_22_t2_r = bf05_22_t2,
bf05_12_t2_r = bf05_12_t2,
bf05_42_t2_r = bf05_42_t2,
bf05_17_t2_r = bf05_17_t2,
bf05_47_t3_r = bf05_47_t3,
bf05_37_t3_r = bf05_37_t3,
bf05_22_t3_r = bf05_22_t3,
bf05_12_t3_r = bf05_12_t3,
bf05_42_t3_r = bf05_42_t3,
bf05_17_t3_r = bf05_17_t3) %>%
mutate(across(c(bf05_47_t1_r, bf05_37_t1_r, bf05_22_t1_r, bf05_12_t1_r, bf05_42_t1_r, bf05_17_t1_r,
bf05_47_t2_r, bf05_37_t2_r, bf05_22_t2_r, bf05_12_t2_r, bf05_42_t2_r, bf05_17_t2_r,
bf05_47_t3_r, bf05_37_t3_r, bf05_22_t3_r, bf05_12_t3_r, bf05_42_t3_r, bf05_17_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(agree_curr_par1_t1 = rowMeans(across(c(bf05_47_t1_r, bf05_57_t1, bf05_07_t1, bf05_17_t1)), na.rm=T),
agree_curr_par2_t1 = rowMeans(across(c(bf05_37_t1_r, bf05_27_t1, bf05_02_t1, bf05_52_t1)), na.rm=T),
agree_curr_par3_t1 = rowMeans(across(c(bf05_22_t1_r, bf05_12_t1_r, bf05_32_t1, bf05_42_t1)), na.rm=T),
agree_curr_par1_t2 = rowMeans(across(c(bf05_47_t2_r, bf05_57_t2, bf05_07_t2, bf05_17_t2)), na.rm=T),
agree_curr_par2_t2 = rowMeans(across(c(bf05_37_t2_r, bf05_27_t2, bf05_02_t2, bf05_52_t2)), na.rm=T),
agree_curr_par3_t2 = rowMeans(across(c(bf05_22_t2_r, bf05_12_t2_r, bf05_32_t2, bf05_42_t2)), na.rm=T),
agree_curr_par1_t3 = rowMeans(across(c(bf05_47_t3_r, bf05_57_t3, bf05_07_t3, bf05_17_t3)), na.rm=T),
agree_curr_par2_t3 = rowMeans(across(c(bf05_37_t3_r, bf05_27_t3, bf05_02_t3, bf05_52_t3)), na.rm=T),
agree_curr_par3_t3 = rowMeans(across(c(bf05_22_t3_r, bf05_12_t3_r, bf05_32_t3, bf05_42_t3)), na.rm=T))Check CFA item loadings at T1
cfa_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*bf06_02_t1 + lambda1*bf06_02_t1 + lambda2*bf06_07_t1 + lambda3*bf06_12_t1 + lambda4*bf06_17_t1 + lambda5*bf06_22_t1 + lambda6*bf06_27_t1 + lambda7*bf06_32_t1 + lambda8*bf06_37_t1 + lambda9*bf06_42_t1 + lambda10*bf06_47_t1 + lambda11*bf06_52_t1 + lambda12*bf06_57_t1
# Intercepts
bf06_02_t1 ~ i1*1
bf06_07_t1 ~ 1
bf06_12_t1 ~ 1
bf06_17_t1 ~ 1
bf06_22_t1 ~ 1
bf06_27_t1 ~ 1
bf06_32_t1 ~ 1
bf06_37_t1 ~ 1
bf06_42_t1 ~ 1
bf06_47_t1 ~ 1
bf06_52_t1 ~ 1
bf06_57_t1 ~ 1
# Unique Variances
bf06_02_t1 ~~ bf06_02_t1
bf06_07_t1 ~~ bf06_07_t1
bf06_12_t1 ~~ bf06_12_t1
bf06_17_t1 ~~ bf06_17_t1
bf06_22_t1 ~~ bf06_22_t1
bf06_27_t1 ~~ bf06_27_t1
bf06_32_t1 ~~ bf06_32_t1
bf06_37_t1 ~~ bf06_37_t1
bf06_42_t1 ~~ bf06_42_t1
bf06_47_t1 ~~ bf06_47_t1
bf06_52_t1 ~~ bf06_52_t1
bf06_57_t1 ~~ bf06_57_t1
# Latent Variable Means
agree_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
'
fit_cfa_agree_ideal <- cfa(cfa_agree_ideal, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_agree_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 42 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 847 849
Number of missing patterns 4
Model Test User Model:
Test statistic 233.018
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2098.126
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.912
Tucker-Lewis Index (TLI) 0.892
Robust Comparative Fit Index (CFI) 0.912
Robust Tucker-Lewis Index (TLI) 0.892
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -13331.073
Loglikelihood unrestricted model (H1) -13214.564
Akaike (AIC) 26734.147
Bayesian (BIC) 26904.848
Sample-size adjusted Bayesian (SABIC) 26790.523
Root Mean Square Error of Approximation:
RMSEA 0.063
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.071
P-value H_0: RMSEA <= 0.050 0.006
P-value H_0: RMSEA >= 0.080 0.000
Robust RMSEA 0.063
90 Percent confidence interval - lower 0.055
90 Percent confidence interval - upper 0.071
P-value H_0: Robust RMSEA <= 0.050 0.006
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.041
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
agree_ideal1 =~
b06_02_ (lmb1) 0.528 0.032 16.391 0.000
b06_07_ (lmb2) 0.365 0.023 15.818 0.000
b06_12_ (lmb3) -0.515 0.035 -14.679 0.000
b06_17_ (lmb4) -0.143 0.059 -2.413 0.016
b06_22_ (lmb5) -0.492 0.037 -13.306 0.000
b06_27_ (lmb6) 0.612 0.037 16.352 0.000
b06_32_ (lmb7) 0.509 0.032 15.871 0.000
b06_37_ (lmb8) -0.511 0.029 -17.449 0.000
b06_42_ (lmb9) -0.462 0.044 -10.587 0.000
b06_47_ (lm10) -0.716 0.037 -19.412 0.000
b06_52_ (lm11) 0.452 0.024 18.543 0.000
b06_57_ (lm12) 0.496 0.037 13.514 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_02_1 (i1) 4.269 0.032 133.231 0.000
.bf06_07_1 4.650 0.023 204.363 0.000
.bf06_12_1 1.913 0.034 55.740 0.000
.bf06_17_1 2.585 0.054 47.665 0.000
.bf06_22_1 1.789 0.036 49.972 0.000
.bf06_27_1 3.911 0.037 104.943 0.000
.bf06_32_1 4.304 0.032 135.497 0.000
.bf06_37_1 1.633 0.029 55.361 0.000
.bf06_42_1 2.758 0.041 66.971 0.000
.bf06_47_1 1.861 0.038 49.122 0.000
.bf06_52_1 4.540 0.025 183.437 0.000
.bf06_57_1 3.787 0.036 106.585 0.000
agree_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_02_t1 0.590 0.032 18.476 0.000
.bf06_07_t1 0.305 0.016 18.590 0.000
.bf06_12_t1 0.731 0.039 18.964 0.000
.bf06_17_t1 2.468 0.120 20.531 0.000
.bf06_22_t1 0.842 0.044 19.317 0.000
.bf06_27_t1 0.801 0.043 18.535 0.000
.bf06_32_t1 0.593 0.032 18.663 0.000
.bf06_37_t1 0.474 0.026 18.155 0.000
.bf06_42_t1 1.220 0.062 19.775 0.000
.bf06_47_t1 0.701 0.040 17.349 0.000
.bf06_52_t1 0.314 0.018 17.648 0.000
.bf06_57_t1 0.822 0.043 19.256 0.000
agree_ideal1 1.000 tidy(fit_cfa_agree_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 agree_ideal1 =~ bf06_47_t1 =~ lambda10 -0.716 0.0369 -0.650
2 agree_ideal1 =~ bf06_27_t1 =~ lambda6 0.612 0.0374 0.565
3 agree_ideal1 =~ bf06_02_t1 =~ lambda1 0.528 0.0322 0.567
4 agree_ideal1 =~ bf06_12_t1 =~ lambda3 -0.515 0.0351 -0.516
5 agree_ideal1 =~ bf06_37_t1 =~ lambda8 -0.511 0.0293 -0.596
6 agree_ideal1 =~ bf06_32_t1 =~ lambda7 0.509 0.0321 0.551
7 agree_ideal1 =~ bf06_57_t1 =~ lambda12 0.496 0.0367 0.480
8 agree_ideal1 =~ bf06_22_t1 =~ lambda5 -0.492 0.0370 -0.473
9 agree_ideal1 =~ bf06_42_t1 =~ lambda9 -0.462 0.0436 -0.386
10 agree_ideal1 =~ bf06_52_t1 =~ lambda11 0.452 0.0244 0.628
11 agree_ideal1 =~ bf06_07_t1 =~ lambda2 0.365 0.0230 0.551
12 agree_ideal1 =~ bf06_17_t1 =~ lambda4 -0.143 0.0594 -0.0909Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf06_47_t1_r = bf06_47_t1,
bf06_37_t1_r = bf06_37_t1,
bf06_22_t1_r = bf06_22_t1,
bf06_12_t1_r = bf06_12_t1,
bf06_42_t1_r = bf06_42_t1,
bf06_17_t1_r = bf06_17_t1,
bf06_47_t2_r = bf06_47_t2,
bf06_37_t2_r = bf06_37_t2,
bf06_22_t2_r = bf06_22_t2,
bf06_12_t2_r = bf06_12_t2,
bf06_42_t2_r = bf06_42_t2,
bf06_17_t2_r = bf06_17_t2,
bf06_47_t3_r = bf06_47_t3,
bf06_37_t3_r = bf06_37_t3,
bf06_22_t3_r = bf06_22_t3,
bf06_12_t3_r = bf06_12_t3,
bf06_42_t3_r = bf06_42_t3,
bf06_17_t3_r = bf06_17_t3) %>%
mutate(across(c(bf06_47_t1_r, bf06_37_t1_r, bf06_22_t1_r, bf06_12_t1_r, bf06_42_t1_r, bf06_17_t1_r,
bf06_47_t2_r, bf06_37_t2_r, bf06_22_t2_r, bf06_12_t2_r, bf06_42_t2_r, bf06_17_t2_r,
bf06_47_t3_r, bf06_37_t3_r, bf06_22_t3_r, bf06_12_t3_r, bf06_42_t3_r, bf06_17_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(agree_ideal_par1_t1 = rowMeans(across(c(bf06_47_t1_r, bf06_32_t1, bf06_42_t1_r, bf06_17_t1_r)), na.rm=T),
agree_ideal_par2_t1 = rowMeans(across(c(bf06_27_t1, bf06_37_t1_r, bf06_22_t1_r, bf06_07_t1)), na.rm=T),
agree_ideal_par3_t1 = rowMeans(across(c(bf06_02_t1, bf06_12_t1_r, bf06_57_t1, bf06_52_t1)), na.rm=T),
agree_ideal_par1_t2 = rowMeans(across(c(bf06_47_t2_r, bf06_32_t2, bf06_42_t2_r, bf06_17_t2_r)), na.rm=T),
agree_ideal_par2_t2 = rowMeans(across(c(bf06_27_t2, bf06_37_t2_r, bf06_22_t2_r, bf06_07_t2)), na.rm=T),
agree_ideal_par3_t2 = rowMeans(across(c(bf06_02_t2, bf06_12_t2_r, bf06_57_t2, bf06_52_t2)), na.rm=T),
agree_ideal_par1_t3 = rowMeans(across(c(bf06_47_t3_r, bf06_32_t3, bf06_42_t3_r, bf06_17_t3_r)), na.rm=T),
agree_ideal_par2_t3 = rowMeans(across(c(bf06_27_t3, bf06_37_t3_r, bf06_22_t3_r, bf06_07_t3)), na.rm=T),
agree_ideal_par3_t3 = rowMeans(across(c(bf06_02_t3, bf06_12_t3_r, bf06_57_t3, bf06_52_t3)), na.rm=T))Check CFA item loadings at T1
cfa_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*bf05_03_t1 + lambda1*bf05_03_t1 + lambda2*bf05_08_t1 + lambda3*bf05_13_t1 + lambda4*bf05_18_t1 + lambda5*bf05_23_t1 + lambda6*bf05_28_t1 + lambda7*bf05_33_t1 + lambda8*bf05_38_t1 + lambda9*bf05_43_t1 + lambda10*bf05_48_t1 + lambda11*bf05_53_t1 + lambda12*bf05_58_t1
# Intercepts
bf05_03_t1 ~ i1*1
bf05_08_t1 ~ 1
bf05_13_t1 ~ 1
bf05_18_t1 ~ 1
bf05_23_t1 ~ 1
bf05_28_t1 ~ 1
bf05_33_t1 ~ 1
bf05_38_t1 ~ 1
bf05_43_t1 ~ 1
bf05_48_t1 ~ 1
bf05_53_t1 ~ 1
bf05_58_t1 ~ 1
# Unique Variances
bf05_03_t1 ~~ bf05_03_t1
bf05_08_t1 ~~ bf05_08_t1
bf05_13_t1 ~~ bf05_13_t1
bf05_18_t1 ~~ bf05_18_t1
bf05_23_t1 ~~ bf05_23_t1
bf05_28_t1 ~~ bf05_28_t1
bf05_33_t1 ~~ bf05_33_t1
bf05_38_t1 ~~ bf05_38_t1
bf05_43_t1 ~~ bf05_43_t1
bf05_48_t1 ~~ bf05_48_t1
bf05_53_t1 ~~ bf05_53_t1
bf05_58_t1 ~~ bf05_58_t1
# Latent Variable Means
consc_curr1 ~ 0*1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
'
fit_cfa_consc_curr <- cfa(cfa_consc_curr, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_consc_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 39 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 4
Model Test User Model:
Test statistic 801.533
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3586.088
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.788
Tucker-Lewis Index (TLI) 0.740
Robust Comparative Fit Index (CFI) 0.788
Robust Tucker-Lewis Index (TLI) 0.740
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -14696.583
Loglikelihood unrestricted model (H1) -14295.816
Akaike (AIC) 29465.166
Bayesian (BIC) 29635.910
Sample-size adjusted Bayesian (SABIC) 29521.584
Root Mean Square Error of Approximation:
RMSEA 0.128
90 Percent confidence interval - lower 0.120
90 Percent confidence interval - upper 0.136
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.128
90 Percent confidence interval - lower 0.120
90 Percent confidence interval - upper 0.136
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.073
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
consc_curr1 =~
b05_03_ (lmb1) 1.014 0.044 23.242 0.000
b05_08_ (lmb2) 0.808 0.042 19.245 0.000
b05_13_ (lmb3) -0.380 0.039 -9.805 0.000
b05_18_ (lmb4) -0.733 0.037 -19.560 0.000
b05_23_ (lmb5) 0.801 0.043 18.440 0.000
b05_28_ (lmb6) 0.720 0.043 16.786 0.000
b05_33_ (lmb7) -0.793 0.037 -21.311 0.000
b05_38_ (lmb8) -0.637 0.033 -19.029 0.000
b05_43_ (lmb9) -0.470 0.034 -13.894 0.000
b05_48_ (lm10) 0.719 0.039 18.599 0.000
b05_53_ (lm11) -0.634 0.037 -17.019 0.000
b05_58_ (lm12) 0.684 0.044 15.680 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_03_1 (i1) 2.894 0.048 60.442 0.000
.bf05_08_1 3.440 0.044 78.102 0.000
.bf05_13_1 3.625 0.037 97.459 0.000
.bf05_18_1 3.576 0.039 90.816 0.000
.bf05_23_1 3.366 0.045 74.573 0.000
.bf05_28_1 2.879 0.044 65.754 0.000
.bf05_33_1 3.409 0.040 85.611 0.000
.bf05_38_1 3.703 0.035 106.023 0.000
.bf05_43_1 3.986 0.033 119.068 0.000
.bf05_48_1 2.094 0.040 52.070 0.000
.bf05_53_1 3.775 0.038 99.069 0.000
.bf05_58_1 2.769 0.044 62.917 0.000
cnsc_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_03_t1 0.915 0.054 17.080 0.000
.bf05_08_t1 0.992 0.054 18.527 0.000
.bf05_13_t1 1.028 0.051 20.118 0.000
.bf05_18_t1 0.776 0.042 18.328 0.000
.bf05_23_t1 1.085 0.058 18.700 0.000
.bf05_28_t1 1.105 0.058 19.059 0.000
.bf05_33_t1 0.714 0.040 17.661 0.000
.bf05_38_t1 0.629 0.034 18.474 0.000
.bf05_43_t1 0.729 0.037 19.538 0.000
.bf05_48_t1 0.854 0.046 18.661 0.000
.bf05_53_t1 0.829 0.044 19.001 0.000
.bf05_58_t1 1.174 0.061 19.285 0.000
consc_curr1 1.000 tidy(fit_cfa_consc_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 consc_curr1 =~ bf05_03_t1 =~ lambda1 1.01 0.0436 0.727
2 consc_curr1 =~ bf05_08_t1 =~ lambda2 0.808 0.0420 0.630
3 consc_curr1 =~ bf05_23_t1 =~ lambda5 0.801 0.0435 0.610
4 consc_curr1 =~ bf05_33_t1 =~ lambda7 -0.793 0.0372 -0.684
5 consc_curr1 =~ bf05_18_t1 =~ lambda4 -0.733 0.0375 -0.640
6 consc_curr1 =~ bf05_28_t1 =~ lambda6 0.720 0.0429 0.565
7 consc_curr1 =~ bf05_48_t1 =~ lambda10 0.719 0.0386 0.614
8 consc_curr1 =~ bf05_58_t1 =~ lambda12 0.684 0.0436 0.534
9 consc_curr1 =~ bf05_38_t1 =~ lambda8 -0.637 0.0335 -0.626
10 consc_curr1 =~ bf05_53_t1 =~ lambda11 -0.634 0.0373 -0.572
11 consc_curr1 =~ bf05_43_t1 =~ lambda9 -0.470 0.0339 -0.483
12 consc_curr1 =~ bf05_13_t1 =~ lambda3 -0.380 0.0387 -0.351Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf05_03_t1_r = bf05_03_t1,
bf05_23_t1_r = bf05_23_t1,
bf05_48_t1_r = bf05_48_t1,
bf05_08_t1_r = bf05_08_t1,
bf05_28_t1_r = bf05_28_t1,
bf05_58_t1_r = bf05_58_t1,
bf05_03_t2_r = bf05_03_t2,
bf05_23_t2_r = bf05_23_t2,
bf05_48_t2_r = bf05_48_t2,
bf05_08_t2_r = bf05_08_t2,
bf05_28_t2_r = bf05_28_t2,
bf05_58_t2_r = bf05_58_t2,
bf05_03_t3_r = bf05_03_t3,
bf05_23_t3_r = bf05_23_t3,
bf05_48_t3_r = bf05_48_t3,
bf05_08_t3_r = bf05_08_t3,
bf05_28_t3_r = bf05_28_t3,
bf05_58_t3_r = bf05_58_t3) %>%
mutate(across(c(bf05_03_t1_r, bf05_23_t1_r, bf05_48_t1_r, bf05_08_t1_r, bf05_28_t1_r, bf05_58_t1_r,
bf05_03_t2_r, bf05_23_t2_r, bf05_48_t2_r, bf05_08_t2_r, bf05_28_t2_r, bf05_58_t2_r,
bf05_03_t3_r, bf05_23_t3_r, bf05_48_t3_r, bf05_08_t3_r, bf05_28_t3_r, bf05_58_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(consc_curr_par1_t1 = rowMeans(across(c(bf05_03_t1_r, bf05_28_t1_r, bf05_38_t1, bf05_13_t1)), na.rm=T),
consc_curr_par2_t1 = rowMeans(across(c(bf05_08_t1_r, bf05_18_t1, bf05_58_t1_r, bf05_43_t1)), na.rm=T),
consc_curr_par3_t1 = rowMeans(across(c(bf05_23_t1_r, bf05_33_t1, bf05_48_t1_r, bf05_53_t1)), na.rm=T),
consc_curr_par1_t2 = rowMeans(across(c(bf05_03_t2_r, bf05_28_t2_r, bf05_38_t2, bf05_13_t2)), na.rm=T),
consc_curr_par2_t2 = rowMeans(across(c(bf05_08_t2_r, bf05_18_t2, bf05_58_t2_r, bf05_43_t2)), na.rm=T),
consc_curr_par3_t2 = rowMeans(across(c(bf05_23_t2_r, bf05_33_t2, bf05_48_t2_r, bf05_53_t2)), na.rm=T),
consc_curr_par1_t3 = rowMeans(across(c(bf05_03_t3_r, bf05_28_t3_r, bf05_38_t3, bf05_13_t3)), na.rm=T),
consc_curr_par2_t3 = rowMeans(across(c(bf05_08_t3_r, bf05_18_t3, bf05_58_t3_r, bf05_43_t3)), na.rm=T),
consc_curr_par3_t3 = rowMeans(across(c(bf05_23_t3_r, bf05_33_t3, bf05_48_t3_r, bf05_53_t3)), na.rm=T))Check CFA item loadings at T1
cfa_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*bf06_03_t1 + lambda1*bf06_03_t1 + lambda2*bf06_08_t1 + lambda3*bf06_13_t1 + lambda4*bf06_18_t1 + lambda5*bf06_23_t1 + lambda6*bf06_28_t1 + lambda7*bf06_33_t1 + lambda8*bf06_38_t1 + lambda9*bf06_43_t1 + lambda10*bf06_48_t1 + lambda11*bf06_53_t1 + lambda12*bf06_58_t1
# Intercepts
bf06_03_t1 ~ i1*1
bf06_08_t1 ~ 1
bf06_13_t1 ~ 1
bf06_18_t1 ~ 1
bf06_23_t1 ~ 1
bf06_28_t1 ~ 1
bf06_33_t1 ~ 1
bf06_38_t1 ~ 1
bf06_43_t1 ~ 1
bf06_48_t1 ~ 1
bf06_53_t1 ~ 1
bf06_58_t1 ~ 1
# Unique Variances
bf06_03_t1 ~~ bf06_03_t1
bf06_08_t1 ~~ bf06_08_t1
bf06_13_t1 ~~ bf06_13_t1
bf06_18_t1 ~~ bf06_18_t1
bf06_23_t1 ~~ bf06_23_t1
bf06_28_t1 ~~ bf06_28_t1
bf06_33_t1 ~~ bf06_33_t1
bf06_38_t1 ~~ bf06_38_t1
bf06_43_t1 ~~ bf06_43_t1
bf06_48_t1 ~~ bf06_48_t1
bf06_53_t1 ~~ bf06_53_t1
bf06_58_t1 ~~ bf06_58_t1
# Latent Variable Means
consc_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
'
fit_cfa_consc_ideal <- cfa(cfa_consc_ideal, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_consc_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 41 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 5
Model Test User Model:
Test statistic 261.132
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2186.521
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.902
Tucker-Lewis Index (TLI) 0.881
Robust Comparative Fit Index (CFI) 0.902
Robust Tucker-Lewis Index (TLI) 0.880
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -11827.022
Loglikelihood unrestricted model (H1) -11696.456
Akaike (AIC) 23726.045
Bayesian (BIC) 23896.788
Sample-size adjusted Bayesian (SABIC) 23782.463
Root Mean Square Error of Approximation:
RMSEA 0.067
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.076
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.005
Robust RMSEA 0.067
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.076
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.006
Standardized Root Mean Square Residual:
SRMR 0.044
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
consc_ideal1 =~
b06_03_ (lmb1) 0.441 0.042 10.435 0.000
b06_08_ (lmb2) 0.412 0.032 12.845 0.000
b06_13_ (lmb3) -0.368 0.042 -8.845 0.000
b06_18_ (lmb4) -0.417 0.028 -14.929 0.000
b06_23_ (lmb5) 0.451 0.029 15.561 0.000
b06_28_ (lmb6) 0.577 0.035 16.264 0.000
b06_33_ (lmb7) -0.403 0.026 -15.487 0.000
b06_38_ (lmb8) -0.445 0.023 -19.306 0.000
b06_43_ (lmb9) -0.419 0.025 -16.913 0.000
b06_48_ (lm10) 0.419 0.027 15.427 0.000
b06_53_ (lm11) -0.433 0.021 -20.174 0.000
b06_58_ (lm12) 0.494 0.034 14.373 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_03_1 (i1) 1.718 0.040 42.978 0.000
.bf06_08_1 1.606 0.031 51.992 0.000
.bf06_13_1 4.196 0.039 107.674 0.000
.bf06_18_1 4.352 0.027 158.625 0.000
.bf06_23_1 1.488 0.029 51.993 0.000
.bf06_28_1 1.857 0.035 52.569 0.000
.bf06_33_1 4.460 0.026 173.589 0.000
.bf06_38_1 4.642 0.024 195.735 0.000
.bf06_43_1 4.565 0.025 183.655 0.000
.bf06_48_1 1.378 0.027 51.299 0.000
.bf06_53_1 4.632 0.022 207.973 0.000
.bf06_58_1 1.775 0.034 52.825 0.000
consc_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_03_t1 1.157 0.058 19.844 0.000
.bf06_08_t1 0.637 0.033 19.365 0.000
.bf06_13_t1 1.151 0.057 20.076 0.000
.bf06_18_t1 0.463 0.024 18.963 0.000
.bf06_23_t1 0.489 0.026 18.752 0.000
.bf06_28_t1 0.723 0.039 18.580 0.000
.bf06_33_t1 0.396 0.021 18.785 0.000
.bf06_38_t1 0.278 0.016 17.526 0.000
.bf06_43_t1 0.347 0.019 18.385 0.000
.bf06_48_t1 0.435 0.023 18.877 0.000
.bf06_53_t1 0.232 0.014 17.105 0.000
.bf06_58_t1 0.711 0.037 19.097 0.000
consc_ideal1 1.000 tidy(fit_cfa_consc_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 consc_ideal1 =~ bf06_28_t1 =~ lambda6 0.577 0.0355 0.562
2 consc_ideal1 =~ bf06_58_t1 =~ lambda12 0.494 0.0344 0.505
3 consc_ideal1 =~ bf06_23_t1 =~ lambda5 0.451 0.0290 0.542
4 consc_ideal1 =~ bf06_38_t1 =~ lambda8 -0.445 0.0231 -0.645
5 consc_ideal1 =~ bf06_03_t1 =~ lambda1 0.441 0.0422 0.379
6 consc_ideal1 =~ bf06_53_t1 =~ lambda11 -0.433 0.0215 -0.668
7 consc_ideal1 =~ bf06_43_t1 =~ lambda9 -0.419 0.0248 -0.580
8 consc_ideal1 =~ bf06_48_t1 =~ lambda10 0.419 0.0272 0.536
9 consc_ideal1 =~ bf06_18_t1 =~ lambda4 -0.417 0.0279 -0.522
10 consc_ideal1 =~ bf06_08_t1 =~ lambda2 0.412 0.0321 0.459
11 consc_ideal1 =~ bf06_33_t1 =~ lambda7 -0.403 0.0260 -0.539
12 consc_ideal1 =~ bf06_13_t1 =~ lambda3 -0.368 0.0416 -0.324Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf06_03_t1_r = bf06_03_t1,
bf06_23_t1_r = bf06_23_t1,
bf06_48_t1_r = bf06_48_t1,
bf06_08_t1_r = bf06_08_t1,
bf06_28_t1_r = bf06_28_t1,
bf06_58_t1_r = bf06_58_t1,
bf06_03_t2_r = bf06_03_t2,
bf06_23_t2_r = bf06_23_t2,
bf06_48_t2_r = bf06_48_t2,
bf06_08_t2_r = bf06_08_t2,
bf06_28_t2_r = bf06_28_t2,
bf06_58_t2_r = bf06_58_t2,
bf06_03_t3_r = bf06_03_t3,
bf06_23_t3_r = bf06_23_t3,
bf06_48_t3_r = bf06_48_t3,
bf06_08_t3_r = bf06_08_t3,
bf06_28_t3_r = bf06_28_t3,
bf06_58_t3_r = bf06_58_t3) %>%
mutate(across(c(bf06_03_t1_r, bf06_23_t1_r, bf06_48_t1_r, bf06_08_t1_r, bf06_28_t1_r, bf06_58_t1_r,
bf06_03_t2_r, bf06_23_t2_r, bf06_48_t2_r, bf06_08_t2_r, bf06_28_t2_r, bf06_58_t2_r,
bf06_03_t3_r, bf06_23_t3_r, bf06_48_t3_r, bf06_08_t3_r, bf06_28_t3_r, bf06_58_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(consc_ideal_par1_t1 = rowMeans(across(c(bf06_28_t1_r, bf06_53_t1, bf06_18_t1, bf06_13_t1)), na.rm=T),
consc_ideal_par2_t1 = rowMeans(across(c(bf06_58_t1_r, bf06_03_t1_r, bf06_48_t1_r, bf06_33_t1)), na.rm=T),
consc_ideal_par3_t1 = rowMeans(across(c(bf06_23_t1_r, bf06_38_t1, bf06_43_t1, bf06_08_t1_r)), na.rm=T),
consc_ideal_par1_t2 = rowMeans(across(c(bf06_28_t2_r, bf06_53_t2, bf06_18_t2, bf06_13_t2)), na.rm=T),
consc_ideal_par2_t2 = rowMeans(across(c(bf06_58_t2_r, bf06_03_t2_r, bf06_48_t2_r, bf06_33_t2)), na.rm=T),
consc_ideal_par3_t2 = rowMeans(across(c(bf06_23_t2_r, bf06_38_t2, bf06_43_t2, bf06_08_t2_r)), na.rm=T),
consc_ideal_par1_t3 = rowMeans(across(c(bf06_28_t3_r, bf06_53_t3, bf06_18_t3, bf06_13_t3)), na.rm=T),
consc_ideal_par2_t3 = rowMeans(across(c(bf06_58_t3_r, bf06_03_t3_r, bf06_48_t3_r, bf06_33_t3)), na.rm=T),
consc_ideal_par3_t3 = rowMeans(across(c(bf06_23_t3_r, bf06_38_t3, bf06_43_t3, bf06_08_t3_r)), na.rm=T))Check CFA item loadings at T1
cfa_neuro_curr <- '
#| warning: false
# Define the latent factors
neuro_curr1 =~ NA*bf05_04_t1 + lambda1*bf05_04_t1 + lambda2*bf05_09_t1 + lambda3*bf05_14_t1 + lambda4*bf05_19_t1 + lambda5*bf05_24_t1 + lambda6*bf05_29_t1 + lambda7*bf05_34_t1 + lambda8*bf05_39_t1 + lambda9*bf05_44_t1 + lambda10*bf05_49_t1 + lambda11*bf05_54_t1 + lambda12*bf05_59_t1
# Intercepts
bf05_04_t1 ~ i1*1
bf05_09_t1 ~ 1
bf05_14_t1 ~ 1
bf05_19_t1 ~ 1
bf05_24_t1 ~ 1
bf05_29_t1 ~ 1
bf05_34_t1 ~ 1
bf05_39_t1 ~ 1
bf05_44_t1 ~ 1
bf05_49_t1 ~ 1
bf05_54_t1 ~ 1
bf05_59_t1 ~ 1
# Unique Variances
bf05_04_t1 ~~ bf05_04_t1
bf05_09_t1 ~~ bf05_09_t1
bf05_14_t1 ~~ bf05_14_t1
bf05_19_t1 ~~ bf05_19_t1
bf05_24_t1 ~~ bf05_24_t1
bf05_29_t1 ~~ bf05_29_t1
bf05_34_t1 ~~ bf05_34_t1
bf05_39_t1 ~~ bf05_39_t1
bf05_44_t1 ~~ bf05_44_t1
bf05_49_t1 ~~ bf05_49_t1
bf05_54_t1 ~~ bf05_54_t1
bf05_59_t1 ~~ bf05_59_t1
# Latent Variable Means
neuro_curr1 ~ 0*1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
'
fit_cfa_neuro_curr <- cfa(cfa_neuro_curr, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
611summary(fit_cfa_neuro_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 35 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 4
Model Test User Model:
Test statistic 680.474
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 4495.728
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.859
Tucker-Lewis Index (TLI) 0.827
Robust Comparative Fit Index (CFI) 0.858
Robust Tucker-Lewis Index (TLI) 0.827
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -14759.163
Loglikelihood unrestricted model (H1) -14418.926
Akaike (AIC) 29590.325
Bayesian (BIC) 29761.069
Sample-size adjusted Bayesian (SABIC) 29646.744
Root Mean Square Error of Approximation:
RMSEA 0.117
90 Percent confidence interval - lower 0.109
90 Percent confidence interval - upper 0.125
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.117
90 Percent confidence interval - lower 0.109
90 Percent confidence interval - upper 0.125
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.055
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_curr1 =~
b05_04_ (lmb1) 0.904 0.041 22.165 0.000
b05_09_ (lmb2) 0.758 0.039 19.533 0.000
b05_14_ (lmb3) -0.901 0.042 -21.239 0.000
b05_19_ (lmb4) -0.624 0.035 -17.726 0.000
b05_24_ (lmb5) 0.755 0.040 18.667 0.000
b05_29_ (lmb6) 0.906 0.038 23.588 0.000
b05_34_ (lmb7) -0.806 0.037 -22.003 0.000
b05_39_ (lmb8) -0.921 0.040 -23.031 0.000
b05_44_ (lmb9) 0.806 0.039 20.762 0.000
b05_49_ (lm10) 0.623 0.041 15.315 0.000
b05_54_ (lm11) -0.996 0.043 -23.246 0.000
b05_59_ (lm12) -0.814 0.042 -19.249 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_04_1 (i1) 2.755 0.045 61.511 0.000
.bf05_09_1 2.933 0.041 70.774 0.000
.bf05_14_1 3.152 0.046 68.350 0.000
.bf05_19_1 3.588 0.037 97.236 0.000
.bf05_24_1 3.017 0.043 70.486 0.000
.bf05_29_1 2.977 0.043 69.428 0.000
.bf05_34_1 3.879 0.040 96.548 0.000
.bf05_39_1 3.303 0.044 74.777 0.000
.bf05_44_1 3.210 0.042 76.710 0.000
.bf05_49_1 2.342 0.042 56.155 0.000
.bf05_54_1 3.249 0.047 68.523 0.000
.bf05_59_1 3.128 0.045 69.737 0.000
neur_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_04_t1 0.882 0.048 18.423 0.000
.bf05_09_t1 0.880 0.046 19.093 0.000
.bf05_14_t1 0.992 0.053 18.707 0.000
.bf05_19_t1 0.765 0.039 19.447 0.000
.bf05_24_t1 0.982 0.051 19.257 0.000
.bf05_29_t1 0.736 0.041 17.947 0.000
.bf05_34_t1 0.719 0.039 18.497 0.000
.bf05_39_t1 0.806 0.045 17.863 0.000
.bf05_44_t1 0.834 0.045 18.629 0.000
.bf05_49_t1 1.084 0.055 19.757 0.000
.bf05_54_t1 0.915 0.052 17.759 0.000
.bf05_59_t1 1.042 0.055 18.985 0.000
neuro_curr1 1.000 tidy(fit_cfa_neuro_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 neuro_curr1 =~ bf05_54_t1 =~ lambda11 -0.996 0.0428 -0.721
2 neuro_curr1 =~ bf05_39_t1 =~ lambda8 -0.921 0.0400 -0.716
3 neuro_curr1 =~ bf05_29_t1 =~ lambda6 0.906 0.0384 0.726
4 neuro_curr1 =~ bf05_04_t1 =~ lambda1 0.904 0.0408 0.693
5 neuro_curr1 =~ bf05_14_t1 =~ lambda3 -0.901 0.0424 -0.671
6 neuro_curr1 =~ bf05_59_t1 =~ lambda12 -0.814 0.0423 -0.624
7 neuro_curr1 =~ bf05_34_t1 =~ lambda7 -0.806 0.0366 -0.689
8 neuro_curr1 =~ bf05_44_t1 =~ lambda9 0.806 0.0388 0.662
9 neuro_curr1 =~ bf05_09_t1 =~ lambda2 0.758 0.0388 0.628
10 neuro_curr1 =~ bf05_24_t1 =~ lambda5 0.755 0.0405 0.606
11 neuro_curr1 =~ bf05_19_t1 =~ lambda4 -0.624 0.0352 -0.581
12 neuro_curr1 =~ bf05_49_t1 =~ lambda10 0.623 0.0407 0.514Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf05_29_t1_r = bf05_29_t1,
bf05_04_t1_r = bf05_04_t1,
bf05_24_t1_r = bf05_24_t1,
bf05_44_t1_r = bf05_44_t1,
bf05_09_t1_r = bf05_09_t1,
bf05_49_t1_r = bf05_49_t1,
bf05_29_t2_r = bf05_29_t2,
bf05_04_t2_r = bf05_04_t2,
bf05_24_t2_r = bf05_24_t2,
bf05_44_t2_r = bf05_44_t2,
bf05_09_t2_r = bf05_09_t2,
bf05_49_t2_r = bf05_49_t2,
bf05_29_t3_r = bf05_29_t3,
bf05_04_t3_r = bf05_04_t3,
bf05_24_t3_r = bf05_24_t3,
bf05_44_t3_r = bf05_44_t3,
bf05_09_t3_r = bf05_09_t3,
bf05_49_t3_r = bf05_49_t3) %>%
mutate(across(c(bf05_29_t1_r, bf05_04_t1_r, bf05_24_t1_r, bf05_44_t1_r, bf05_09_t1_r, bf05_49_t1_r,
bf05_29_t2_r, bf05_04_t2_r, bf05_24_t2_r, bf05_44_t2_r, bf05_09_t2_r, bf05_49_t2_r,
bf05_29_t3_r, bf05_04_t3_r, bf05_24_t3_r, bf05_44_t3_r, bf05_09_t3_r, bf05_49_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(neuro_curr_par1_t1 = rowMeans(across(c(bf05_54_t1, bf05_59_t1, bf05_09_t1_r, bf05_49_t1_r)), na.rm=T),
neuro_curr_par2_t1 = rowMeans(across(c(bf05_39_t1, bf05_14_t1, bf05_44_t1_r, bf05_19_t1)), na.rm=T),
neuro_curr_par3_t1 = rowMeans(across(c(bf05_29_t1_r, bf05_04_t1_r, bf05_34_t1, bf05_24_t1_r)), na.rm=T),
neuro_curr_par1_t2 = rowMeans(across(c(bf05_54_t2, bf05_59_t2, bf05_09_t2_r, bf05_49_t2_r)), na.rm=T),
neuro_curr_par2_t2 = rowMeans(across(c(bf05_39_t2, bf05_14_t2, bf05_44_t2_r, bf05_19_t2)), na.rm=T),
neuro_curr_par3_t2 = rowMeans(across(c(bf05_29_t2_r, bf05_04_t2_r, bf05_34_t2, bf05_24_t2_r)), na.rm=T),
neuro_curr_par1_t3 = rowMeans(across(c(bf05_54_t3, bf05_59_t3, bf05_09_t3_r, bf05_49_t3_r)), na.rm=T),
neuro_curr_par2_t3 = rowMeans(across(c(bf05_39_t3, bf05_14_t3, bf05_44_t3_r, bf05_19_t3)), na.rm=T),
neuro_curr_par3_t3 = rowMeans(across(c(bf05_29_t3_r, bf05_04_t3_r, bf05_34_t3, bf05_24_t3_r)), na.rm=T))Check CFA item loadings at T1
cfa_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*bf06_04_t1 + lambda1*bf06_04_t1 + lambda2*bf06_09_t1 + lambda3*bf06_14_t1 + lambda4*bf06_19_t1 + lambda5*bf06_24_t1 + lambda6*bf06_29_t1 + lambda7*bf06_34_t1 + lambda8*bf06_39_t1 + lambda9*bf06_44_t1 + lambda10*bf06_49_t1 + lambda11*bf06_54_t1 + lambda12*bf06_59_t1
# Intercepts
bf06_04_t1 ~ i1*1
bf06_09_t1 ~ 1
bf06_14_t1 ~ 1
bf06_19_t1 ~ 1
bf06_24_t1 ~ 1
bf06_29_t1 ~ 1
bf06_34_t1 ~ 1
bf06_39_t1 ~ 1
bf06_44_t1 ~ 1
bf06_49_t1 ~ 1
bf06_54_t1 ~ 1
bf06_59_t1 ~ 1
# Unique Variances
bf06_04_t1 ~~ bf06_04_t1
bf06_09_t1 ~~ bf06_09_t1
bf06_14_t1 ~~ bf06_14_t1
bf06_19_t1 ~~ bf06_19_t1
bf06_24_t1 ~~ bf06_24_t1
bf06_29_t1 ~~ bf06_29_t1
bf06_34_t1 ~~ bf06_34_t1
bf06_39_t1 ~~ bf06_39_t1
bf06_44_t1 ~~ bf06_44_t1
bf06_49_t1 ~~ bf06_49_t1
bf06_54_t1 ~~ bf06_54_t1
bf06_59_t1 ~~ bf06_59_t1
# Latent Variable Means
neuro_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
'
fit_cfa_neuro_ideal <- cfa(cfa_neuro_ideal, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_neuro_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 41 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 5
Model Test User Model:
Test statistic 273.694
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2229.774
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.898
Tucker-Lewis Index (TLI) 0.876
Robust Comparative Fit Index (CFI) 0.898
Robust Tucker-Lewis Index (TLI) 0.876
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12149.997
Loglikelihood unrestricted model (H1) -12013.150
Akaike (AIC) 24371.994
Bayesian (BIC) 24542.738
Sample-size adjusted Bayesian (SABIC) 24428.413
Root Mean Square Error of Approximation:
RMSEA 0.069
90 Percent confidence interval - lower 0.061
90 Percent confidence interval - upper 0.078
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.016
Robust RMSEA 0.069
90 Percent confidence interval - lower 0.061
90 Percent confidence interval - upper 0.078
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.017
Standardized Root Mean Square Residual:
SRMR 0.042
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_ideal1 =~
b06_04_ (lmb1) 0.311 0.027 11.743 0.000
b06_09_ (lmb2) 0.430 0.026 16.554 0.000
b06_14_ (lmb3) -0.492 0.028 -17.268 0.000
b06_19_ (lmb4) -0.525 0.032 -16.181 0.000
b06_24_ (lmb5) 0.355 0.025 14.392 0.000
b06_29_ (lmb6) 0.409 0.039 10.403 0.000
b06_34_ (lmb7) -0.548 0.032 -16.987 0.000
b06_39_ (lmb8) -0.524 0.027 -19.366 0.000
b06_44_ (lmb9) 0.462 0.027 16.874 0.000
b06_49_ (lm10) 0.321 0.051 6.247 0.000
b06_54_ (lm11) -0.533 0.026 -20.319 0.000
b06_59_ (lm12) -0.479 0.035 -13.698 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_04_1 (i1) 4.549 0.025 178.914 0.000
.bf06_09_1 4.441 0.026 170.833 0.000
.bf06_14_1 1.609 0.029 56.032 0.000
.bf06_19_1 1.985 0.032 61.253 0.000
.bf06_24_1 4.664 0.024 193.333 0.000
.bf06_29_1 4.251 0.037 114.255 0.000
.bf06_34_1 1.816 0.032 55.945 0.000
.bf06_39_1 1.565 0.028 56.296 0.000
.bf06_44_1 4.425 0.027 161.125 0.000
.bf06_49_1 3.816 0.048 80.268 0.000
.bf06_54_1 1.468 0.027 53.906 0.000
.bf06_59_1 1.903 0.034 56.042 0.000
neuro_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_04_t1 0.451 0.023 19.693 0.000
.bf06_09_t1 0.387 0.021 18.568 0.000
.bf06_14_t1 0.455 0.025 18.321 0.000
.bf06_19_t1 0.612 0.033 18.691 0.000
.bf06_24_t1 0.366 0.019 19.077 0.000
.bf06_29_t1 1.005 0.051 19.860 0.000
.bf06_34_t1 0.591 0.032 18.447 0.000
.bf06_39_t1 0.379 0.022 17.415 0.000
.bf06_44_t1 0.425 0.023 18.437 0.000
.bf06_49_t1 1.811 0.089 20.344 0.000
.bf06_54_t1 0.344 0.020 17.009 0.000
.bf06_59_t1 0.747 0.039 19.249 0.000
neuro_ideal1 1.000 tidy(fit_cfa_neuro_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 neuro_ideal1 =~ bf06_34_t1 =~ lambda7 -0.548 0.0323 -0.581
2 neuro_ideal1 =~ bf06_54_t1 =~ lambda11 -0.533 0.0262 -0.672
3 neuro_ideal1 =~ bf06_19_t1 =~ lambda4 -0.525 0.0325 -0.558
4 neuro_ideal1 =~ bf06_39_t1 =~ lambda8 -0.524 0.0271 -0.648
5 neuro_ideal1 =~ bf06_14_t1 =~ lambda3 -0.492 0.0285 -0.589
6 neuro_ideal1 =~ bf06_59_t1 =~ lambda12 -0.479 0.0349 -0.484
7 neuro_ideal1 =~ bf06_44_t1 =~ lambda9 0.462 0.0274 0.579
8 neuro_ideal1 =~ bf06_09_t1 =~ lambda2 0.430 0.0260 0.569
9 neuro_ideal1 =~ bf06_29_t1 =~ lambda6 0.409 0.0393 0.378
10 neuro_ideal1 =~ bf06_24_t1 =~ lambda5 0.355 0.0247 0.506
11 neuro_ideal1 =~ bf06_49_t1 =~ lambda10 0.321 0.0513 0.232
12 neuro_ideal1 =~ bf06_04_t1 =~ lambda1 0.311 0.0265 0.421Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf06_29_t1_r = bf06_29_t1,
bf06_04_t1_r = bf06_04_t1,
bf06_24_t1_r = bf06_24_t1,
bf06_44_t1_r = bf06_44_t1,
bf06_09_t1_r = bf06_09_t1,
bf06_49_t1_r = bf06_49_t1,
bf06_29_t2_r = bf06_29_t2,
bf06_04_t2_r = bf06_04_t2,
bf06_24_t2_r = bf06_24_t2,
bf06_44_t2_r = bf06_44_t2,
bf06_09_t2_r = bf06_09_t2,
bf06_49_t2_r = bf06_49_t2,
bf06_29_t3_r = bf06_29_t3,
bf06_04_t3_r = bf06_04_t3,
bf06_24_t3_r = bf06_24_t3,
bf06_44_t3_r = bf06_44_t3,
bf06_09_t3_r = bf06_09_t3,
bf06_49_t3_r = bf06_49_t3) %>%
mutate(across(c(bf06_29_t1_r, bf06_04_t1_r, bf06_24_t1_r, bf06_44_t1_r, bf06_09_t1_r, bf06_49_t1_r,
bf06_29_t2_r, bf06_04_t2_r, bf06_24_t2_r, bf06_44_t2_r, bf06_09_t2_r, bf06_49_t2_r,
bf06_29_t3_r, bf06_04_t3_r, bf06_24_t3_r, bf06_44_t3_r, bf06_09_t3_r, bf06_49_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(neuro_ideal_par1_t1 = rowMeans(across(c(bf06_34_t1, bf06_59_t1, bf06_29_t1_r, bf06_04_t1_r)), na.rm=T),
neuro_ideal_par2_t1 = rowMeans(across(c(bf06_54_t1, bf06_14_t1, bf06_09_t1_r, bf06_49_t1_r)), na.rm=T),
neuro_ideal_par3_t1 = rowMeans(across(c(bf06_19_t1, bf06_39_t1, bf06_44_t1_r, bf06_24_t1_r)), na.rm=T),
neuro_ideal_par1_t2 = rowMeans(across(c(bf06_34_t2, bf06_59_t2, bf06_29_t2_r, bf06_04_t2_r)), na.rm=T),
neuro_ideal_par2_t2 = rowMeans(across(c(bf06_54_t2, bf06_14_t2, bf06_09_t2_r, bf06_49_t2_r)), na.rm=T),
neuro_ideal_par3_t2 = rowMeans(across(c(bf06_19_t2, bf06_39_t2, bf06_44_t2_r, bf06_24_t2_r)), na.rm=T),
neuro_ideal_par1_t3 = rowMeans(across(c(bf06_34_t3, bf06_59_t3, bf06_29_t3_r, bf06_04_t3_r)), na.rm=T),
neuro_ideal_par2_t3 = rowMeans(across(c(bf06_54_t3, bf06_14_t3, bf06_09_t3_r, bf06_49_t3_r)), na.rm=T),
neuro_ideal_par3_t3 = rowMeans(across(c(bf06_19_t3, bf06_39_t3, bf06_44_t3_r, bf06_24_t3_r)), na.rm=T))Check CFA item loadings at T1
cfa_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*bf05_05_t1 + lambda1*bf05_05_t1 + lambda2*bf05_10_t1 + lambda3*bf05_15_t1 + lambda4*bf05_20_t1 + lambda5*bf05_25_t1 + lambda6*bf05_30_t1 + lambda7*bf05_35_t1 + lambda8*bf05_40_t1 + lambda9*bf05_45_t1 + lambda10*bf05_50_t1 + lambda11*bf05_55_t1 + lambda12*bf05_60_t1
# Intercepts
bf05_05_t1 ~ i1*1
bf05_10_t1 ~ 1
bf05_15_t1 ~ 1
bf05_20_t1 ~ 1
bf05_25_t1 ~ 1
bf05_30_t1 ~ 1
bf05_35_t1 ~ 1
bf05_40_t1 ~ 1
bf05_45_t1 ~ 1
bf05_50_t1 ~ 1
bf05_55_t1 ~ 1
bf05_60_t1 ~ 1
# Unique Variances
bf05_05_t1 ~~ bf05_05_t1
bf05_10_t1 ~~ bf05_10_t1
bf05_15_t1 ~~ bf05_15_t1
bf05_20_t1 ~~ bf05_20_t1
bf05_25_t1 ~~ bf05_25_t1
bf05_30_t1 ~~ bf05_30_t1
bf05_35_t1 ~~ bf05_35_t1
bf05_40_t1 ~~ bf05_40_t1
bf05_45_t1 ~~ bf05_45_t1
bf05_50_t1 ~~ bf05_50_t1
bf05_55_t1 ~~ bf05_55_t1
bf05_60_t1 ~~ bf05_60_t1
# Latent Variable Means
openn_curr1 ~ 0*1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
'
fit_cfa_openn_curr <- cfa(cfa_openn_curr, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_openn_curr, fit.measures = TRUE)lavaan 0.6.15 ended normally after 37 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 848 849
Number of missing patterns 2
Model Test User Model:
Test statistic 686.354
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2445.809
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.734
Tucker-Lewis Index (TLI) 0.675
Robust Comparative Fit Index (CFI) 0.734
Robust Tucker-Lewis Index (TLI) 0.675
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -14957.504
Loglikelihood unrestricted model (H1) -14614.327
Akaike (AIC) 29987.007
Bayesian (BIC) 30157.751
Sample-size adjusted Bayesian (SABIC) 30043.426
Root Mean Square Error of Approximation:
RMSEA 0.118
90 Percent confidence interval - lower 0.110
90 Percent confidence interval - upper 0.125
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.118
90 Percent confidence interval - lower 0.110
90 Percent confidence interval - upper 0.126
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.071
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
openn_curr1 =~
b05_05_ (lmb1) 0.291 0.050 5.788 0.000
b05_10_ (lmb2) -0.455 0.033 -13.723 0.000
b05_15_ (lmb3) -0.517 0.038 -13.574 0.000
b05_20_ (lmb4) -0.756 0.042 -18.203 0.000
b05_25_ (lmb5) 0.565 0.046 12.321 0.000
b05_30_ (lmb6) 0.622 0.044 14.134 0.000
b05_35_ (lmb7) -0.688 0.037 -18.439 0.000
b05_40_ (lmb8) -0.546 0.036 -15.159 0.000
b05_45_ (lmb9) 0.562 0.042 13.394 0.000
b05_50_ (lm10) 0.700 0.048 14.469 0.000
b05_55_ (lm11) 0.623 0.044 14.239 0.000
b05_60_ (lm12) -0.597 0.038 -15.746 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_05_1 (i1) 2.927 0.046 63.757 0.000
.bf05_10_1 4.107 0.032 128.627 0.000
.bf05_15_1 3.642 0.036 101.863 0.000
.bf05_20_1 3.862 0.040 95.629 0.000
.bf05_25_1 2.363 0.043 54.499 0.000
.bf05_30_1 2.473 0.042 58.767 0.000
.bf05_35_1 3.999 0.036 109.961 0.000
.bf05_40_1 3.968 0.035 113.040 0.000
.bf05_45_1 2.099 0.040 52.442 0.000
.bf05_50_1 2.399 0.046 51.800 0.000
.bf05_55_1 2.435 0.042 57.439 0.000
.bf05_60_1 3.539 0.037 96.935 0.000
opnn_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_05_t1 1.703 0.084 20.359 0.000
.bf05_10_t1 0.658 0.034 19.091 0.000
.bf05_15_t1 0.817 0.044 18.616 0.000
.bf05_20_t1 0.811 0.050 16.084 0.000
.bf05_25_t1 1.276 0.066 19.321 0.000
.bf05_30_t1 1.114 0.059 18.722 0.000
.bf05_35_t1 0.647 0.041 15.919 0.000
.bf05_40_t1 0.747 0.040 18.676 0.000
.bf05_45_t1 1.041 0.055 19.015 0.000
.bf05_50_t1 1.326 0.072 18.546 0.000
.bf05_55_t1 1.134 0.060 18.955 0.000
.bf05_60_t1 0.773 0.043 17.908 0.000
openn_curr1 1.000 tidy(fit_cfa_openn_curr) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 openn_curr1 =~ bf05_20_t1 =~ lambda4 -0.756 0.0416 -0.643
2 openn_curr1 =~ bf05_50_t1 =~ lambda10 0.700 0.0484 0.520
3 openn_curr1 =~ bf05_35_t1 =~ lambda7 -0.688 0.0373 -0.650
4 openn_curr1 =~ bf05_55_t1 =~ lambda11 0.623 0.0438 0.505
5 openn_curr1 =~ bf05_30_t1 =~ lambda6 0.622 0.0440 0.508
6 openn_curr1 =~ bf05_60_t1 =~ lambda12 -0.597 0.0379 -0.562
7 openn_curr1 =~ bf05_25_t1 =~ lambda5 0.565 0.0458 0.447
8 openn_curr1 =~ bf05_45_t1 =~ lambda9 0.562 0.0420 0.483
9 openn_curr1 =~ bf05_40_t1 =~ lambda8 -0.546 0.0360 -0.534
10 openn_curr1 =~ bf05_15_t1 =~ lambda3 -0.517 0.0381 -0.496
11 openn_curr1 =~ bf05_10_t1 =~ lambda2 -0.455 0.0332 -0.489
12 openn_curr1 =~ bf05_05_t1 =~ lambda1 0.291 0.0502 0.217Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf05_50_t1_r = bf05_50_t1,
bf05_55_t1_r = bf05_55_t1,
bf05_30_t1_r = bf05_30_t1,
bf05_25_t1_r = bf05_25_t1,
bf05_45_t1_r = bf05_45_t1,
bf05_05_t1_r = bf05_05_t1,
bf05_50_t2_r = bf05_50_t2,
bf05_55_t2_r = bf05_55_t2,
bf05_30_t2_r = bf05_30_t2,
bf05_25_t2_r = bf05_25_t2,
bf05_45_t2_r = bf05_45_t2,
bf05_05_t2_r = bf05_05_t2,
bf05_50_t3_r = bf05_50_t3,
bf05_55_t3_r = bf05_55_t3,
bf05_30_t3_r = bf05_30_t3,
bf05_25_t3_r = bf05_25_t3,
bf05_45_t3_r = bf05_45_t3,
bf05_05_t3_r = bf05_05_t3) %>%
mutate(across(c(bf05_50_t1_r, bf05_55_t1_r, bf05_30_t1_r, bf05_25_t1_r, bf05_45_t1_r, bf05_05_t1_r,
bf05_50_t2_r, bf05_55_t2_r, bf05_30_t2_r, bf05_25_t2_r, bf05_45_t2_r, bf05_05_t2_r,
bf05_50_t3_r, bf05_55_t3_r, bf05_30_t3_r, bf05_25_t3_r, bf05_45_t3_r, bf05_05_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(openn_curr_par1_t1 = rowMeans(across(c(bf05_20_t1, bf05_60_t1, bf05_25_t1_r, bf05_05_t1_r)), na.rm=T),
openn_curr_par2_t1 = rowMeans(across(c(bf05_50_t1_r, bf05_30_t1_r, bf05_45_t1_r, bf05_10_t1)), na.rm=T),
openn_curr_par3_t1 = rowMeans(across(c(bf05_35_t1, bf05_55_t1_r, bf05_40_t1, bf05_15_t1)), na.rm=T),
openn_curr_par1_t2 = rowMeans(across(c(bf05_20_t2, bf05_60_t2, bf05_25_t2_r, bf05_05_t2_r)), na.rm=T),
openn_curr_par2_t2 = rowMeans(across(c(bf05_50_t2_r, bf05_30_t2_r, bf05_45_t2_r, bf05_10_t2)), na.rm=T),
openn_curr_par3_t2 = rowMeans(across(c(bf05_35_t2, bf05_55_t2_r, bf05_40_t2, bf05_15_t2)), na.rm=T),
openn_curr_par1_t3 = rowMeans(across(c(bf05_20_t3, bf05_60_t3, bf05_25_t3_r, bf05_05_t3_r)), na.rm=T),
openn_curr_par2_t3 = rowMeans(across(c(bf05_50_t3_r, bf05_30_t3_r, bf05_45_t3_r, bf05_10_t3)), na.rm=T),
openn_curr_par3_t3 = rowMeans(across(c(bf05_35_t3, bf05_55_t3_r, bf05_40_t3, bf05_15_t3)), na.rm=T))Check CFA item loadings at T1
cfa_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*bf06_05_t1 + lambda1*bf06_05_t1 + lambda2*bf06_10_t1 + lambda3*bf06_15_t1 + lambda4*bf06_20_t1 + lambda5*bf06_25_t1 + lambda6*bf06_30_t1 + lambda7*bf06_35_t1 + lambda8*bf06_40_t1 + lambda9*bf06_45_t1 + lambda10*bf06_50_t1 + lambda11*bf06_55_t1 + lambda12*bf06_60_t1
# Intercepts
bf06_05_t1 ~ i1*1
bf06_10_t1 ~ 1
bf06_15_t1 ~ 1
bf06_20_t1 ~ 1
bf06_25_t1 ~ 1
bf06_30_t1 ~ 1
bf06_35_t1 ~ 1
bf06_40_t1 ~ 1
bf06_45_t1 ~ 1
bf06_50_t1 ~ 1
bf06_55_t1 ~ 1
bf06_60_t1 ~ 1
# Unique Variances
bf06_05_t1 ~~ bf06_05_t1
bf06_10_t1 ~~ bf06_10_t1
bf06_15_t1 ~~ bf06_15_t1
bf06_20_t1 ~~ bf06_20_t1
bf06_25_t1 ~~ bf06_25_t1
bf06_30_t1 ~~ bf06_30_t1
bf06_35_t1 ~~ bf06_35_t1
bf06_40_t1 ~~ bf06_40_t1
bf06_45_t1 ~~ bf06_45_t1
bf06_50_t1 ~~ bf06_50_t1
bf06_55_t1 ~~ bf06_55_t1
bf06_60_t1 ~~ bf06_60_t1
# Latent Variable Means
openn_ideal1 ~ 0*1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
'
fit_cfa_openn_ideal <- cfa(cfa_openn_ideal, data = df_sbsa2_wide_pers, mimic = "mplus", missing="ML")
summary(fit_cfa_openn_ideal, fit.measures = TRUE)lavaan 0.6.15 ended normally after 43 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 847 849
Number of missing patterns 5
Model Test User Model:
Test statistic 460.240
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2053.128
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.796
Tucker-Lewis Index (TLI) 0.750
Robust Comparative Fit Index (CFI) 0.796
Robust Tucker-Lewis Index (TLI) 0.751
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -13794.655
Loglikelihood unrestricted model (H1) -13564.536
Akaike (AIC) 27661.311
Bayesian (BIC) 27832.012
Sample-size adjusted Bayesian (SABIC) 27717.687
Root Mean Square Error of Approximation:
RMSEA 0.094
90 Percent confidence interval - lower 0.086
90 Percent confidence interval - upper 0.102
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.998
Robust RMSEA 0.095
90 Percent confidence interval - lower 0.087
90 Percent confidence interval - upper 0.103
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.999
Standardized Root Mean Square Residual:
SRMR 0.062
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
openn_ideal1 =~
b06_05_ (lmb1) 0.255 0.057 4.476 0.000
b06_10_ (lmb2) -0.418 0.027 -15.710 0.000
b06_15_ (lmb3) -0.383 0.025 -15.319 0.000
b06_20_ (lmb4) -0.548 0.033 -16.798 0.000
b06_25_ (lmb5) 0.537 0.039 13.632 0.000
b06_30_ (lmb6) 0.559 0.052 10.808 0.000
b06_35_ (lmb7) -0.545 0.034 -16.236 0.000
b06_40_ (lmb8) -0.511 0.042 -12.236 0.000
b06_45_ (lmb9) 0.525 0.038 13.904 0.000
b06_50_ (lm10) 0.590 0.041 14.308 0.000
b06_55_ (lm11) 0.554 0.045 12.323 0.000
b06_60_ (lm12) -0.456 0.027 -17.166 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf06_05_1 (i1) 3.020 0.051 59.265 0.000
.bf06_10_1 4.492 0.026 173.867 0.000
.bf06_15_1 4.595 0.024 191.631 0.000
.bf06_20_1 4.331 0.032 137.244 0.000
.bf06_25_1 1.823 0.037 48.684 0.000
.bf06_30_1 2.012 0.048 42.001 0.000
.bf06_35_1 4.296 0.032 133.053 0.000
.bf06_40_1 3.899 0.039 99.042 0.000
.bf06_45_1 1.639 0.036 45.721 0.000
.bf06_50_1 1.996 0.039 50.743 0.000
.bf06_55_1 2.159 0.042 51.370 0.000
.bf06_60_1 4.528 0.026 174.121 0.000
openn_dl1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf06_05_t1 2.129 0.104 20.396 0.000
.bf06_10_t1 0.389 0.021 18.294 0.000
.bf06_15_t1 0.340 0.019 18.225 0.000
.bf06_20_t1 0.541 0.031 17.325 0.000
.bf06_25_t1 0.897 0.047 18.909 0.000
.bf06_30_t1 1.630 0.083 19.553 0.000
.bf06_35_t1 0.583 0.033 17.571 0.000
.bf06_40_t1 1.049 0.054 19.317 0.000
.bf06_45_t1 0.812 0.043 18.754 0.000
.bf06_50_t1 0.961 0.052 18.632 0.000
.bf06_55_t1 1.188 0.062 19.162 0.000
.bf06_60_t1 0.365 0.021 17.350 0.000
openn_ideal1 1.000 tidy(fit_cfa_openn_ideal) %>% filter(str_detect(label, "lambda")) %>% mutate(abs_loading = abs(estimate)) %>% arrange(desc(abs_loading)) %>% select(1:5, std.all)# A tibble: 12 × 6
term op label estimate std.error std.all
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 openn_ideal1 =~ bf06_50_t1 =~ lambda10 0.590 0.0412 0.516
2 openn_ideal1 =~ bf06_30_t1 =~ lambda6 0.559 0.0517 0.401
3 openn_ideal1 =~ bf06_55_t1 =~ lambda11 0.554 0.0449 0.453
4 openn_ideal1 =~ bf06_20_t1 =~ lambda4 -0.548 0.0326 -0.598
5 openn_ideal1 =~ bf06_35_t1 =~ lambda7 -0.545 0.0336 -0.581
6 openn_ideal1 =~ bf06_25_t1 =~ lambda5 0.537 0.0394 0.493
7 openn_ideal1 =~ bf06_45_t1 =~ lambda9 0.525 0.0377 0.503
8 openn_ideal1 =~ bf06_40_t1 =~ lambda8 -0.511 0.0418 -0.447
9 openn_ideal1 =~ bf06_60_t1 =~ lambda12 -0.456 0.0266 -0.603
10 openn_ideal1 =~ bf06_10_t1 =~ lambda2 -0.418 0.0266 -0.556
11 openn_ideal1 =~ bf06_15_t1 =~ lambda3 -0.383 0.0250 -0.549
12 openn_ideal1 =~ bf06_05_t1 =~ lambda1 0.255 0.0569 0.172Reverse-code and form parcels:
df_sbsa2_wide_pers <- df_sbsa2_wide_pers %>%
mutate(bf06_50_t1_r = bf06_50_t1,
bf06_55_t1_r = bf06_55_t1,
bf06_30_t1_r = bf06_30_t1,
bf06_25_t1_r = bf06_25_t1,
bf06_45_t1_r = bf06_45_t1,
bf06_05_t1_r = bf06_05_t1,
bf06_50_t2_r = bf06_50_t2,
bf06_55_t2_r = bf06_55_t2,
bf06_30_t2_r = bf06_30_t2,
bf06_25_t2_r = bf06_25_t2,
bf06_45_t2_r = bf06_45_t2,
bf06_05_t2_r = bf06_05_t2,
bf06_50_t3_r = bf06_50_t3,
bf06_55_t3_r = bf06_55_t3,
bf06_30_t3_r = bf06_30_t3,
bf06_25_t3_r = bf06_25_t3,
bf06_45_t3_r = bf06_45_t3,
bf06_05_t3_r = bf06_05_t3) %>%
mutate(across(c(bf06_50_t1_r, bf06_55_t1_r, bf06_30_t1_r, bf06_25_t1_r, bf06_45_t1_r, bf06_05_t1_r,
bf06_50_t2_r, bf06_55_t2_r, bf06_30_t2_r, bf06_25_t2_r, bf06_45_t2_r, bf06_05_t2_r,
bf06_50_t3_r, bf06_55_t3_r, bf06_30_t3_r, bf06_25_t3_r, bf06_45_t3_r, bf06_05_t3_r),
~ recode(.x, `1` = 5L, `2` = 4L, `3` = 3L, `4` = 2L, `5` = 1L, .default = NA_integer_))) %>%
mutate(openn_ideal_par1_t1 = rowMeans(across(c(bf06_50_t1_r, bf06_25_t1_r, bf06_60_t1, bf06_05_t1)), na.rm=T),
openn_ideal_par2_t1 = rowMeans(across(c(bf06_30_t1_r, bf06_35_t1, bf06_40_t1, bf06_15_t1)), na.rm=T),
openn_ideal_par3_t1 = rowMeans(across(c(bf06_55_t1_r, bf06_20_t1, bf06_45_t1_r, bf06_10_t1)), na.rm=T),
openn_ideal_par1_t2 = rowMeans(across(c(bf06_50_t2_r, bf06_25_t2_r, bf06_60_t2, bf06_05_t2)), na.rm=T),
openn_ideal_par2_t2 = rowMeans(across(c(bf06_30_t2_r, bf06_35_t2, bf06_40_t2, bf06_15_t2)), na.rm=T),
openn_ideal_par3_t2 = rowMeans(across(c(bf06_55_t2_r, bf06_20_t2, bf06_45_t2_r, bf06_10_t2)), na.rm=T),
openn_ideal_par1_t3 = rowMeans(across(c(bf06_50_t3_r, bf06_25_t3_r, bf06_60_t3, bf06_05_t3)), na.rm=T),
openn_ideal_par2_t3 = rowMeans(across(c(bf06_30_t3_r, bf06_35_t3, bf06_40_t3, bf06_15_t3)), na.rm=T),
openn_ideal_par3_t3 = rowMeans(across(c(bf06_55_t3_r, bf06_20_t3, bf06_45_t3_r, bf06_10_t3)), na.rm=T))Testing for measurement invariance of the latent traits across time. Only doing this for the two (original) intervention groups for now, not for the waitlist control group, because they had different intervention schedules and our main focus lies on testing changes in the intervention groups.
Satisfaction with life scale (only the first four items - last one taps into a somewhat different construct)
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + sw06_02_t1 + sw06_03_t1 + sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + sw06_02_t2 + sw06_03_t2 + sw06_04_t2
swls3 =~ NA*sw06_01_t3 + lambda1*sw06_01_t3 + sw06_02_t3 + sw06_03_t3 + sw06_04_t3
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ 1
sw06_03_t1 ~ 1
sw06_04_t1 ~ 1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ 1
sw06_03_t2 ~ 1
sw06_04_t2 ~ 1
sw06_01_t3 ~ i1*1
sw06_02_t3 ~ 1
sw06_03_t3 ~ 1
sw06_04_t3 ~ 1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
sw06_01_t3 ~~ sw06_01_t3
sw06_02_t3 ~~ sw06_02_t3
sw06_03_t3 ~~ sw06_03_t3
sw06_04_t3 ~~ sw06_04_t3
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
swls3 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls3 ~~ swls3
swls1 ~~ swls2
swls1 ~~ swls3
swls2 ~~ swls3
'
fit_configural_swls <- cfa(configural_swls, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_swls, fit.measures = TRUE)
# Weak invariance model
weak_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
swls3 =~ NA*sw06_01_t3 + lambda1*sw06_01_t3 + lambda2*sw06_02_t3 + lambda3*sw06_03_t3 + lambda4*sw06_04_t3
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ 1
sw06_03_t1 ~ 1
sw06_04_t1 ~ 1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ 1
sw06_03_t2 ~ 1
sw06_04_t2 ~ 1
sw06_01_t3 ~ i1*1
sw06_02_t3 ~ 1
sw06_03_t3 ~ 1
sw06_04_t3 ~ 1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
sw06_01_t3 ~~ sw06_01_t3
sw06_02_t3 ~~ sw06_02_t3
sw06_03_t3 ~~ sw06_03_t3
sw06_04_t3 ~~ sw06_04_t3
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
swls3 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls3 ~~ swls3
swls1 ~~ swls2
swls1 ~~ swls3
swls2 ~~ swls3
'
fit_weak_swls <- cfa(weak_swls, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_swls, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
swls3 =~ NA*sw06_01_t3 + lambda1*sw06_01_t3 + lambda2*sw06_02_t3 + lambda3*sw06_03_t3 + lambda4*sw06_04_t3
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ i2*1
sw06_03_t1 ~ i3*1
sw06_04_t1 ~ i4*1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ i2*1
sw06_03_t2 ~ i3*1
sw06_04_t2 ~ i4*1
sw06_01_t3 ~ i1*1
sw06_02_t3 ~ i2*1
sw06_03_t3 ~ i3*1
sw06_04_t3 ~ i4*1
# Unique Variances
sw06_01_t1 ~~ sw06_01_t1
sw06_02_t1 ~~ sw06_02_t1
sw06_03_t1 ~~ sw06_03_t1
sw06_04_t1 ~~ sw06_04_t1
sw06_01_t2 ~~ sw06_01_t2
sw06_02_t2 ~~ sw06_02_t2
sw06_03_t2 ~~ sw06_03_t2
sw06_04_t2 ~~ sw06_04_t2
sw06_01_t3 ~~ sw06_01_t3
sw06_02_t3 ~~ sw06_02_t3
sw06_03_t3 ~~ sw06_03_t3
sw06_04_t3 ~~ sw06_04_t3
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
swls3 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls3 ~~ swls3
swls1 ~~ swls2
swls1 ~~ swls3
swls2 ~~ swls3
'
fit_strong_swls <- cfa(strong_swls, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_swls, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_swls <- '
# Define the latent factors
swls1 =~ NA*sw06_01_t1 + lambda1*sw06_01_t1 + lambda2*sw06_02_t1 + lambda3*sw06_03_t1 + lambda4*sw06_04_t1
swls2 =~ NA*sw06_01_t2 + lambda1*sw06_01_t2 + lambda2*sw06_02_t2 + lambda3*sw06_03_t2 + lambda4*sw06_04_t2
swls3 =~ NA*sw06_01_t3 + lambda1*sw06_01_t3 + lambda2*sw06_02_t3 + lambda3*sw06_03_t3 + lambda4*sw06_04_t3
# Intercepts
sw06_01_t1 ~ i1*1
sw06_02_t1 ~ i2*1
sw06_03_t1 ~ i3*1
sw06_04_t1 ~ i4*1
sw06_01_t2 ~ i1*1
sw06_02_t2 ~ i2*1
sw06_03_t2 ~ i3*1
sw06_04_t2 ~ i4*1
sw06_01_t3 ~ i1*1
sw06_02_t3 ~ i2*1
sw06_03_t3 ~ i3*1
sw06_04_t3 ~ i4*1
# Unique Variances
sw06_01_t1 ~~ u1*sw06_01_t1
sw06_02_t1 ~~ u2*sw06_02_t1
sw06_03_t1 ~~ u3*sw06_03_t1
sw06_04_t1 ~~ u4*sw06_04_t1
sw06_01_t2 ~~ u1*sw06_01_t2
sw06_02_t2 ~~ u2*sw06_02_t2
sw06_03_t2 ~~ u3*sw06_03_t2
sw06_04_t2 ~~ u4*sw06_04_t2
sw06_01_t3 ~~ u1*sw06_01_t3
sw06_02_t3 ~~ u2*sw06_02_t3
sw06_03_t3 ~~ u3*sw06_03_t3
sw06_04_t3 ~~ u4*sw06_04_t3
# Latent Variable Means
swls1 ~ 0*1
swls2 ~ 1
swls3 ~ 1
# Latent Variable Variances and Covariance
swls1 ~~ 1*swls1
swls2 ~~ swls2
swls3 ~~ swls3
swls1 ~~ swls2
swls2 ~~ swls3
swls1 ~~ swls3
'
fit_strict_swls <- cfa(strict_swls, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_swls, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_swls) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 848 39 678. 22384. 22569. 0.907 0.880 0.120 0.0468
2 weak 848 33 678. 22372. 22529. 0.908 0.893 0.113 0.0472
3 strong 848 27 688. 22370. 22498. 0.907 0.903 0.108 0.0485
4 strict 848 19 744. 22410. 22500. 0.900 0.907 0.106 0.0543# chi-square difference test for nested models
anova(fit_configural_swls, fit_weak_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_configural_swls 51 22384 22568 677.81
fit_weak_swls 57 22372 22529 678.41 0.59442 0 6 0.9965anova(fit_weak_swls, fit_strong_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_swls 57 22372 22529 678.41
fit_strong_swls 63 22370 22498 687.90 9.4974 0.026218 6 0.1475anova(fit_strong_swls, fit_strict_swls)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_swls 63 22370 22498 687.90
fit_strict_swls 71 22410 22500 743.84 55.932 0.084056 8 2.908e-09
fit_strong_swls
fit_strict_swls ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + meaning_par2_t1 + meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + meaning_par2_t2 + meaning_par3_t2
meaning3 =~ NA*meaning_par1_t3 + lambda1*meaning_par1_t3 + meaning_par2_t3 + meaning_par3_t3
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ 1
meaning_par3_t1 ~ 1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ 1
meaning_par3_t2 ~ 1
meaning_par1_t3 ~ i1*1
meaning_par2_t3 ~ 1
meaning_par3_t3 ~ 1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
meaning_par1_t3 ~~ meaning_par1_t3
meaning_par2_t3 ~~ meaning_par2_t3
meaning_par3_t3 ~~ meaning_par3_t3
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
meaning3 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning3 ~~ meaning3
meaning1 ~~ meaning2
meaning2 ~~ meaning3
meaning1 ~~ meaning3
'
fit_configural_meaning <- cfa(configural_meaning, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_meaning, fit.measures = TRUE)
# Weak invariance model
weak_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
meaning3 =~ NA*meaning_par1_t3 + lambda1*meaning_par1_t3 + lambda2*meaning_par2_t3 + lambda3*meaning_par3_t3
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ 1
meaning_par3_t1 ~ 1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ 1
meaning_par3_t2 ~ 1
meaning_par1_t3 ~ i1*1
meaning_par2_t3 ~ 1
meaning_par3_t3 ~ 1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
meaning_par1_t3 ~~ meaning_par1_t3
meaning_par2_t3 ~~ meaning_par2_t3
meaning_par3_t3 ~~ meaning_par3_t3
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
meaning3 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning3 ~~ meaning3
meaning1 ~~ meaning2
meaning2 ~~ meaning3
meaning1 ~~ meaning3
'
fit_weak_meaning <- cfa(weak_meaning, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_meaning, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
meaning3 =~ NA*meaning_par1_t3 + lambda1*meaning_par1_t3 + lambda2*meaning_par2_t3 + lambda3*meaning_par3_t3
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ i2*1
meaning_par3_t1 ~ i3*1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ i2*1
meaning_par3_t2 ~ i3*1
meaning_par1_t3 ~ i1*1
meaning_par2_t3 ~ i2*1
meaning_par3_t3 ~ i3*1
# Unique Variances
meaning_par1_t1 ~~ meaning_par1_t1
meaning_par2_t1 ~~ meaning_par2_t1
meaning_par3_t1 ~~ meaning_par3_t1
meaning_par1_t2 ~~ meaning_par1_t2
meaning_par2_t2 ~~ meaning_par2_t2
meaning_par3_t2 ~~ meaning_par3_t2
meaning_par1_t3 ~~ meaning_par1_t3
meaning_par2_t3 ~~ meaning_par2_t3
meaning_par3_t3 ~~ meaning_par3_t3
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
meaning3 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning3 ~~ meaning3
meaning1 ~~ meaning2
meaning1 ~~ meaning3
meaning2 ~~ meaning3
'
fit_strong_meaning <- cfa(strong_meaning, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_meaning, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_meaning <- '
# Define the latent factors
meaning1 =~ NA*meaning_par1_t1 + lambda1*meaning_par1_t1 + lambda2*meaning_par2_t1 + lambda3*meaning_par3_t1
meaning2 =~ NA*meaning_par1_t2 + lambda1*meaning_par1_t2 + lambda2*meaning_par2_t2 + lambda3*meaning_par3_t2
meaning3 =~ NA*meaning_par1_t3 + lambda1*meaning_par1_t3 + lambda2*meaning_par2_t3 + lambda3*meaning_par3_t3
# Intercepts
meaning_par1_t1 ~ i1*1
meaning_par2_t1 ~ i2*1
meaning_par3_t1 ~ i3*1
meaning_par1_t2 ~ i1*1
meaning_par2_t2 ~ i2*1
meaning_par3_t2 ~ i3*1
meaning_par1_t3 ~ i1*1
meaning_par2_t3 ~ i2*1
meaning_par3_t3 ~ i3*1
# Unique Variances
meaning_par1_t1 ~~ u1*meaning_par1_t1
meaning_par2_t1 ~~ u2*meaning_par2_t1
meaning_par3_t1 ~~ u3*meaning_par3_t1
meaning_par1_t2 ~~ u1*meaning_par1_t2
meaning_par2_t2 ~~ u2*meaning_par2_t2
meaning_par3_t2 ~~ u3*meaning_par3_t2
meaning_par1_t3 ~~ u1*meaning_par1_t3
meaning_par2_t3 ~~ u2*meaning_par2_t3
meaning_par3_t3 ~~ u3*meaning_par3_t3
# Latent Variable Means
meaning1 ~ 0*1
meaning2 ~ 1
meaning3 ~ 1
# Latent Variable Variances and Covariance
meaning1 ~~ 1*meaning1
meaning2 ~~ meaning2
meaning3 ~~ meaning3
meaning1 ~~ meaning2
meaning1 ~~ meaning3
meaning2 ~~ meaning3
'
fit_strict_meaning <- cfa(strict_meaning, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_meaning, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_meaning) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 848 30 561. 16778. 16920. 0.910 0.865 0.162 0.0390
2 weak 848 26 566. 16775. 16899. 0.910 0.884 0.151 0.0423
3 strong 848 22 568. 16769. 16874. 0.910 0.899 0.141 0.0420
4 strict 848 16 586. 16775. 16851. 0.908 0.913 0.130 0.0476# chi-square difference test for nested models
anova(fit_configural_meaning, fit_weak_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_meaning 24 16778 16920 561.41
fit_weak_meaning 28 16775 16898 566.50 5.0933 0.017954 4
Pr(>Chisq)
fit_configural_meaning
fit_weak_meaning 0.2779anova(fit_weak_meaning, fit_strong_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_meaning 28 16775 16898 566.50
fit_strong_meaning 32 16769 16874 568.49 1.9918 0 4 0.7373anova(fit_strong_meaning, fit_strict_meaning)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_meaning 32 16769 16874 568.49
fit_strict_meaning 38 16775 16851 586.21 17.716 0.047987 6 0.006982
fit_strong_meaning
fit_strict_meaning **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + selfes_par2_t1 + selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + selfes_par2_t2 + selfes_par3_t2
selfes3 =~ NA*selfes_par1_t3 + lambda1*selfes_par1_t3 + selfes_par2_t3 + selfes_par3_t3
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ 1
selfes_par3_t1 ~ 1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ 1
selfes_par3_t2 ~ 1
selfes_par1_t3 ~ i1*1
selfes_par2_t3 ~ 1
selfes_par3_t3 ~ 1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
selfes_par1_t3 ~~ selfes_par1_t3
selfes_par2_t3 ~~ selfes_par2_t3
selfes_par3_t3 ~~ selfes_par3_t3
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
selfes3 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes3 ~~ selfes3
selfes1 ~~ selfes2
selfes1 ~~ selfes3
selfes2 ~~ selfes3
'
fit_configural_selfes <- cfa(configural_selfes, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_selfes, fit.measures = TRUE)
# Weak invariance model
weak_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
selfes3 =~ NA*selfes_par1_t3 + lambda1*selfes_par1_t3 + lambda2*selfes_par2_t3 + lambda3*selfes_par3_t3
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ 1
selfes_par3_t1 ~ 1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ 1
selfes_par3_t2 ~ 1
selfes_par1_t3 ~ i1*1
selfes_par2_t3 ~ 1
selfes_par3_t3 ~ 1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
selfes_par1_t3 ~~ selfes_par1_t3
selfes_par2_t3 ~~ selfes_par2_t3
selfes_par3_t3 ~~ selfes_par3_t3
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
selfes3 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes3 ~~ selfes3
selfes1 ~~ selfes2
selfes1 ~~ selfes3
selfes2 ~~ selfes3
'
fit_weak_selfes <- cfa(weak_selfes, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_selfes, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
selfes3 =~ NA*selfes_par1_t3 + lambda1*selfes_par1_t3 + lambda2*selfes_par2_t3 + lambda3*selfes_par3_t3
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ i2*1
selfes_par3_t1 ~ i3*1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ i2*1
selfes_par3_t2 ~ i3*1
selfes_par1_t3 ~ i1*1
selfes_par2_t3 ~ i2*1
selfes_par3_t3 ~ i3*1
# Unique Variances
selfes_par1_t1 ~~ selfes_par1_t1
selfes_par2_t1 ~~ selfes_par2_t1
selfes_par3_t1 ~~ selfes_par3_t1
selfes_par1_t2 ~~ selfes_par1_t2
selfes_par2_t2 ~~ selfes_par2_t2
selfes_par3_t2 ~~ selfes_par3_t2
selfes_par1_t3 ~~ selfes_par1_t3
selfes_par2_t3 ~~ selfes_par2_t3
selfes_par3_t3 ~~ selfes_par3_t3
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
selfes3 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes3 ~~ selfes3
selfes1 ~~ selfes2
selfes1 ~~ selfes3
selfes2 ~~ selfes3
'
fit_strong_selfes <- cfa(strong_selfes, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_selfes, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_selfes <- '
# Define the latent factors
selfes1 =~ NA*selfes_par1_t1 + lambda1*selfes_par1_t1 + lambda2*selfes_par2_t1 + lambda3*selfes_par3_t1
selfes2 =~ NA*selfes_par1_t2 + lambda1*selfes_par1_t2 + lambda2*selfes_par2_t2 + lambda3*selfes_par3_t2
selfes3 =~ NA*selfes_par1_t3 + lambda1*selfes_par1_t3 + lambda2*selfes_par2_t3 + lambda3*selfes_par3_t3
# Intercepts
selfes_par1_t1 ~ i1*1
selfes_par2_t1 ~ i2*1
selfes_par3_t1 ~ i3*1
selfes_par1_t2 ~ i1*1
selfes_par2_t2 ~ i2*1
selfes_par3_t2 ~ i3*1
selfes_par1_t3 ~ i1*1
selfes_par2_t3 ~ i2*1
selfes_par3_t3 ~ i3*1
# Unique Variances
selfes_par1_t1 ~~ u1*selfes_par1_t1
selfes_par2_t1 ~~ u2*selfes_par2_t1
selfes_par3_t1 ~~ u3*selfes_par3_t1
selfes_par1_t2 ~~ u1*selfes_par1_t2
selfes_par2_t2 ~~ u2*selfes_par2_t2
selfes_par3_t2 ~~ u3*selfes_par3_t2
selfes_par1_t3 ~~ u1*selfes_par1_t3
selfes_par2_t3 ~~ u2*selfes_par2_t3
selfes_par3_t3 ~~ u3*selfes_par3_t3
# Latent Variable Means
selfes1 ~ 0*1
selfes2 ~ 1
selfes3 ~ 1
# Latent Variable Variances and Covariance
selfes1 ~~ 1*selfes1
selfes2 ~~ selfes2
selfes3 ~~ selfes3
selfes1 ~~ selfes2
selfes1 ~~ selfes3
selfes2 ~~ selfes3
'
fit_strict_selfes <- cfa(strict_selfes, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_selfes, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_selfes) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 848 30 520. 12326. 12468. 0.924 0.886 0.156 0.0379
2 weak 848 26 523. 12320. 12443. 0.924 0.902 0.144 0.0407
3 strong 848 22 526. 12315. 12419. 0.924 0.915 0.135 0.0400
4 strict 848 16 532. 12310. 12385. 0.924 0.928 0.124 0.0427# chi-square difference test for nested models
anova(fit_configural_selfes, fit_weak_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_configural_selfes 24 12326 12468 520.25
fit_weak_selfes 28 12320 12444 522.65 2.3939 0 4 0.6637anova(fit_weak_selfes, fit_strong_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_selfes 28 12320 12444 522.65
fit_strong_selfes 32 12315 12419 525.54 2.8952 0 4 0.5755anova(fit_strong_selfes, fit_strict_selfes)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_selfes 32 12315 12419 525.54
fit_strict_selfes 38 12310 12386 532.10 6.5563 0.010456 6 0.3638Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + concept_par2_t1 + concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + concept_par2_t2 + concept_par3_t2
concept3 =~ NA*concept_par1_t3 + lambda1*concept_par1_t3 + concept_par2_t3 + concept_par3_t3
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ 1
concept_par3_t1 ~ 1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ 1
concept_par3_t2 ~ 1
concept_par1_t3 ~ i1*1
concept_par2_t3 ~ 1
concept_par3_t3 ~ 1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
concept_par1_t3 ~~ concept_par1_t3
concept_par2_t3 ~~ concept_par2_t3
concept_par3_t3 ~~ concept_par3_t3
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
concept3 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept3 ~~ concept3
concept1 ~~ concept2
concept1 ~~ concept3
concept2 ~~ concept3
'
fit_configural_concept <- cfa(configural_concept, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_configural_concept, fit.measures = TRUE)
# Weak invariance model
weak_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
concept3 =~ NA*concept_par1_t3 + lambda1*concept_par1_t3 + lambda2*concept_par2_t3 + lambda3*concept_par3_t3
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ 1
concept_par3_t1 ~ 1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ 1
concept_par3_t2 ~ 1
concept_par1_t3 ~ i1*1
concept_par2_t3 ~ 1
concept_par3_t3 ~ 1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
concept_par1_t3 ~~ concept_par1_t3
concept_par2_t3 ~~ concept_par2_t3
concept_par3_t3 ~~ concept_par3_t3
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
concept3 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept3 ~~ concept3
concept1 ~~ concept2
concept1 ~~ concept3
concept2 ~~ concept3
'
fit_weak_concept <- cfa(weak_concept, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_weak_concept, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
concept3 =~ NA*concept_par1_t3 + lambda1*concept_par1_t3 + lambda2*concept_par2_t3 + lambda3*concept_par3_t3
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ i2*1
concept_par3_t1 ~ i3*1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ i2*1
concept_par3_t2 ~ i3*1
concept_par1_t3 ~ i1*1
concept_par2_t3 ~ i2*1
concept_par3_t3 ~ i3*1
# Unique Variances
concept_par1_t1 ~~ concept_par1_t1
concept_par2_t1 ~~ concept_par2_t1
concept_par3_t1 ~~ concept_par3_t1
concept_par1_t2 ~~ concept_par1_t2
concept_par2_t2 ~~ concept_par2_t2
concept_par3_t2 ~~ concept_par3_t2
concept_par1_t3 ~~ concept_par1_t3
concept_par2_t3 ~~ concept_par2_t3
concept_par3_t3 ~~ concept_par3_t3
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
concept3 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept3 ~~ concept3
concept1 ~~ concept2
concept1 ~~ concept3
concept2 ~~ concept3
'
fit_strong_concept <- cfa(strong_concept, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strong_concept, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_concept <- '
# Define the latent factors
concept1 =~ NA*concept_par1_t1 + lambda1*concept_par1_t1 + lambda2*concept_par2_t1 + lambda3*concept_par3_t1
concept2 =~ NA*concept_par1_t2 + lambda1*concept_par1_t2 + lambda2*concept_par2_t2 + lambda3*concept_par3_t2
concept3 =~ NA*concept_par1_t3 + lambda1*concept_par1_t3 + lambda2*concept_par2_t3 + lambda3*concept_par3_t3
# Intercepts
concept_par1_t1 ~ i1*1
concept_par2_t1 ~ i2*1
concept_par3_t1 ~ i3*1
concept_par1_t2 ~ i1*1
concept_par2_t2 ~ i2*1
concept_par3_t2 ~ i3*1
concept_par1_t3 ~ i1*1
concept_par2_t3 ~ i2*1
concept_par3_t3 ~ i3*1
# Unique Variances
concept_par1_t1 ~~ u1*concept_par1_t1
concept_par2_t1 ~~ u2*concept_par2_t1
concept_par3_t1 ~~ u3*concept_par3_t1
concept_par1_t2 ~~ u1*concept_par1_t2
concept_par2_t2 ~~ u2*concept_par2_t2
concept_par3_t2 ~~ u3*concept_par3_t2
concept_par1_t3 ~~ u1*concept_par1_t3
concept_par2_t3 ~~ u2*concept_par2_t3
concept_par3_t3 ~~ u3*concept_par3_t3
# Latent Variable Means
concept1 ~ 0*1
concept2 ~ 1
concept3 ~ 1
# Latent Variable Variances and Covariance
concept1 ~~ 1*concept1
concept2 ~~ concept2
concept3 ~~ concept3
concept1 ~~ concept2
concept1 ~~ concept3
concept2 ~~ concept3
'
fit_strict_concept <- cfa(strict_concept, data = df_sbsa2_wide_wb, mimic = "mplus", missing="ML")
summary(fit_strict_concept, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_concept) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 848 30 393. 12613. 12755. 0.940 0.910 0.135 0.0290
2 weak 848 26 399. 12611. 12734. 0.940 0.923 0.125 0.0346
3 strong 848 22 401. 12605. 12710. 0.940 0.933 0.117 0.0351
4 strict 848 16 414. 12606. 12682. 0.939 0.942 0.108 0.0336# chi-square difference test for nested models
anova(fit_configural_concept, fit_weak_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_concept 24 12613 12755 392.65
fit_weak_concept 28 12611 12734 399.02 6.3783 0.026479 4
Pr(>Chisq)
fit_configural_concept
fit_weak_concept 0.1726anova(fit_weak_concept, fit_strong_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_weak_concept 28 12611 12734 399.02
fit_strong_concept 32 12605 12710 401.24 2.2189 0 4 0.6956anova(fit_strong_concept, fit_strict_concept)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
fit_strong_concept 32 12605 12710 401.24
fit_strict_concept 38 12606 12682 413.94 12.7 0.036287 6 0.04806
fit_strong_concept
fit_strict_concept *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Testing for measurement invariance of the latent BFI-2 traits across time, from T1 to T2.
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + extra_curr_par2_t1 + extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + extra_curr_par2_t2 + extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ 1
extra_curr_par3_t1 ~ 1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_configural_extra_curr <- cfa(configural_extra_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_extra_curr, fit.measures = TRUE)
# Weak invariance model
weak_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ 1
extra_curr_par3_t1 ~ 1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_weak_extra_curr <- cfa(weak_extra_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_extra_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ i2*1
extra_curr_par3_t1 ~ i3*1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
# Unique Variances
extra_curr_par1_t1 ~~ extra_curr_par1_t1
extra_curr_par2_t1 ~~ extra_curr_par2_t1
extra_curr_par3_t1 ~~ extra_curr_par3_t1
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strong_extra_curr <- cfa(strong_extra_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_extra_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t1 + lambda1*extra_curr_par1_t1 + lambda2*extra_curr_par2_t1 + lambda3*extra_curr_par3_t1
extra_curr2 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
# Intercepts
extra_curr_par1_t1 ~ i1*1
extra_curr_par2_t1 ~ i2*1
extra_curr_par3_t1 ~ i3*1
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
# Unique Variances
extra_curr_par1_t1 ~~ u1*extra_curr_par1_t1
extra_curr_par2_t1 ~~ u2*extra_curr_par2_t1
extra_curr_par3_t1 ~~ u3*extra_curr_par3_t1
extra_curr_par1_t2 ~~ u1*extra_curr_par1_t2
extra_curr_par2_t2 ~~ u2*extra_curr_par2_t2
extra_curr_par3_t2 ~~ u3*extra_curr_par3_t2
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strict_extra_curr <- cfa(strict_extra_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_extra_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 425. 7518. 7603. 0.814 0.651 0.278 0.0710
2 weak 674 17 426. 7515. 7592. 0.814 0.721 0.249 0.0727
3 strong 674 15 441. 7525. 7593. 0.809 0.761 0.230 0.0739
4 strict 674 12 442. 7520. 7574. 0.810 0.810 0.205 0.0764# chi-square difference test for nested models
anova(fit_configural_extra_curr, fit_weak_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_extra_curr 8 7517.6 7603.4 425.15
fit_weak_extra_curr 10 7514.8 7591.6 426.37 1.2267 0 2
Pr(>Chisq)
fit_configural_extra_curr
fit_weak_extra_curr 0.5415anova(fit_weak_extra_curr, fit_strong_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_extra_curr 10 7514.8 7591.6 426.37
fit_strong_extra_curr 12 7525.0 7592.7 440.53 14.159 0.094974 2
Pr(>Chisq)
fit_weak_extra_curr
fit_strong_extra_curr 0.0008422 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_strong_extra_curr, fit_strict_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_extra_curr 12 7525.0 7592.7 440.53
fit_strict_extra_curr 15 7520.2 7574.4 441.73 1.1995 0 3
Pr(>Chisq)
fit_strong_extra_curr
fit_strict_extra_curr 0.7531Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + extra_ideal_par2_t1 + extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + extra_ideal_par2_t2 + extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ 1
extra_ideal_par3_t1 ~ 1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_configural_extra_ideal <- cfa(configural_extra_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_extra_ideal, fit.measures = TRUE)
# Weak invariance model
weak_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ 1
extra_ideal_par3_t1 ~ 1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_weak_extra_ideal <- cfa(weak_extra_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_extra_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ i2*1
extra_ideal_par3_t1 ~ i3*1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
# Unique Variances
extra_ideal_par1_t1 ~~ extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strong_extra_ideal <- cfa(strong_extra_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_extra_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t1 + lambda1*extra_ideal_par1_t1 + lambda2*extra_ideal_par2_t1 + lambda3*extra_ideal_par3_t1
extra_ideal2 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
# Intercepts
extra_ideal_par1_t1 ~ i1*1
extra_ideal_par2_t1 ~ i2*1
extra_ideal_par3_t1 ~ i3*1
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
# Unique Variances
extra_ideal_par1_t1 ~~ u1*extra_ideal_par1_t1
extra_ideal_par2_t1 ~~ u2*extra_ideal_par2_t1
extra_ideal_par3_t1 ~~ u3*extra_ideal_par3_t1
extra_ideal_par1_t2 ~~ u1*extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ u2*extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ u3*extra_ideal_par3_t2
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strict_extra_ideal <- cfa(strict_extra_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_extra_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 164. 6257. 6343. 0.855 0.728 0.170 0.0577
2 weak 674 17 165. 6254. 6331. 0.856 0.783 0.152 0.0624
3 strong 674 15 166. 6251. 6319. 0.856 0.820 0.138 0.0645
4 strict 674 12 172. 6251. 6305. 0.854 0.854 0.125 0.0796# chi-square difference test for nested models
anova(fit_configural_extra_ideal, fit_weak_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_extra_ideal 8 6256.9 6342.6 163.9
fit_weak_extra_ideal 10 6254.1 6330.8 165.1 1.1911 0 2
Pr(>Chisq)
fit_configural_extra_ideal
fit_weak_extra_ideal 0.5513anova(fit_weak_extra_ideal, fit_strong_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_extra_ideal 10 6254.1 6330.8 165.1
fit_strong_extra_ideal 12 6251.3 6319.0 166.3 1.2085 0 2
Pr(>Chisq)
fit_weak_extra_ideal
fit_strong_extra_ideal 0.5465anova(fit_strong_extra_ideal, fit_strict_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_extra_ideal 12 6251.3 6319.0 166.30
fit_strict_extra_ideal 15 6251.1 6305.2 172.11 5.801 0.037219 3
Pr(>Chisq)
fit_strong_extra_ideal
fit_strict_extra_ideal 0.1217Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + agree_curr_par2_t1 + agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + agree_curr_par2_t2 + agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ 1
agree_curr_par3_t1 ~ 1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_configural_agree_curr <- cfa(configural_agree_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_agree_curr, fit.measures = TRUE)
# Weak invariance model
weak_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ 1
agree_curr_par3_t1 ~ 1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_weak_agree_curr <- cfa(weak_agree_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_agree_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ i2*1
agree_curr_par3_t1 ~ i3*1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
# Unique Variances
agree_curr_par1_t1 ~~ agree_curr_par1_t1
agree_curr_par2_t1 ~~ agree_curr_par2_t1
agree_curr_par3_t1 ~~ agree_curr_par3_t1
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strong_agree_curr <- cfa(strong_agree_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_agree_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t1 + lambda1*agree_curr_par1_t1 + lambda2*agree_curr_par2_t1 + lambda3*agree_curr_par3_t1
agree_curr2 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
# Intercepts
agree_curr_par1_t1 ~ i1*1
agree_curr_par2_t1 ~ i2*1
agree_curr_par3_t1 ~ i3*1
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
# Unique Variances
agree_curr_par1_t1 ~~ u1*agree_curr_par1_t1
agree_curr_par2_t1 ~~ u2*agree_curr_par2_t1
agree_curr_par3_t1 ~~ u3*agree_curr_par3_t1
agree_curr_par1_t2 ~~ u1*agree_curr_par1_t2
agree_curr_par2_t2 ~~ u2*agree_curr_par2_t2
agree_curr_par3_t2 ~~ u3*agree_curr_par3_t2
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strict_agree_curr <- cfa(strict_agree_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_agree_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 198. 6007. 6092. 0.866 0.749 0.188 0.0694
2 weak 674 17 199. 6003. 6080. 0.867 0.801 0.167 0.0739
3 strong 674 15 199. 6000. 6067. 0.868 0.835 0.152 0.0747
4 strict 674 12 200. 5994. 6048. 0.870 0.870 0.135 0.0726# chi-square difference test for nested models
anova(fit_configural_agree_curr, fit_weak_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_agree_curr 8 6006.6 6092.3 198.11
fit_weak_agree_curr 10 6003.4 6080.2 198.99 0.87407 0 2
Pr(>Chisq)
fit_configural_agree_curr
fit_weak_agree_curr 0.646anova(fit_weak_agree_curr, fit_strong_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_agree_curr 10 6003.4 6080.2 198.99
fit_strong_agree_curr 12 5999.6 6067.3 199.12 0.12713 0 2
Pr(>Chisq)
fit_weak_agree_curr
fit_strong_agree_curr 0.9384anova(fit_strong_agree_curr, fit_strict_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_agree_curr 12 5999.6 6067.3 199.12
fit_strict_agree_curr 15 5994.3 6048.5 199.88 0.76272 0 3
Pr(>Chisq)
fit_strong_agree_curr
fit_strict_agree_curr 0.8584Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + agree_ideal_par2_t1 + agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + agree_ideal_par2_t2 + agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ 1
agree_ideal_par3_t1 ~ 1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_configural_agree_ideal <- cfa(configural_agree_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_agree_ideal, fit.measures = TRUE)
# Weak invariance model
weak_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ 1
agree_ideal_par3_t1 ~ 1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_weak_agree_ideal <- cfa(weak_agree_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_agree_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ i2*1
agree_ideal_par3_t1 ~ i3*1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
# Unique Variances
agree_ideal_par1_t1 ~~ agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strong_agree_ideal <- cfa(strong_agree_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_agree_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t1 + lambda1*agree_ideal_par1_t1 + lambda2*agree_ideal_par2_t1 + lambda3*agree_ideal_par3_t1
agree_ideal2 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
# Intercepts
agree_ideal_par1_t1 ~ i1*1
agree_ideal_par2_t1 ~ i2*1
agree_ideal_par3_t1 ~ i3*1
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
# Unique Variances
agree_ideal_par1_t1 ~~ u1*agree_ideal_par1_t1
agree_ideal_par2_t1 ~~ u2*agree_ideal_par2_t1
agree_ideal_par3_t1 ~~ u3*agree_ideal_par3_t1
agree_ideal_par1_t2 ~~ u1*agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ u2*agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ u3*agree_ideal_par3_t2
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strict_agree_ideal <- cfa(strict_agree_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_agree_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 156. 5948. 6034. 0.907 0.826 0.166 0.0511
2 weak 674 17 157. 5945. 6021. 0.908 0.862 0.148 0.0554
3 strong 674 15 157. 5941. 6009. 0.909 0.886 0.134 0.0555
4 strict 674 12 166. 5944. 5998. 0.905 0.905 0.122 0.0743# chi-square difference test for nested models
anova(fit_configural_agree_ideal, fit_weak_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_agree_ideal 8 5947.8 6033.6 155.87
fit_weak_agree_ideal 10 5944.7 6021.4 156.77 0.90156 0 2
Pr(>Chisq)
fit_configural_agree_ideal
fit_weak_agree_ideal 0.6371anova(fit_weak_agree_ideal, fit_strong_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_agree_ideal 10 5944.7 6021.4 156.77
fit_strong_agree_ideal 12 5941.1 6008.8 157.12 0.34684 0 2
Pr(>Chisq)
fit_weak_agree_ideal
fit_strong_agree_ideal 0.8408anova(fit_strong_agree_ideal, fit_strict_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_agree_ideal 12 5941.1 6008.8 157.12
fit_strict_agree_ideal 15 5944.2 5998.4 166.26 9.1354 0.055085 3
Pr(>Chisq)
fit_strong_agree_ideal
fit_strict_agree_ideal 0.02754 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + consc_curr_par2_t1 + consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + consc_curr_par2_t2 + consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ 1
consc_curr_par3_t1 ~ 1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_configural_consc_curr <- cfa(configural_consc_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_consc_curr, fit.measures = TRUE)
# Weak invariance model
weak_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ 1
consc_curr_par3_t1 ~ 1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_weak_consc_curr <- cfa(weak_consc_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_consc_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ i2*1
consc_curr_par3_t1 ~ i3*1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
# Unique Variances
consc_curr_par1_t1 ~~ consc_curr_par1_t1
consc_curr_par2_t1 ~~ consc_curr_par2_t1
consc_curr_par3_t1 ~~ consc_curr_par3_t1
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strong_consc_curr <- cfa(strong_consc_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_consc_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t1 + lambda1*consc_curr_par1_t1 + lambda2*consc_curr_par2_t1 + lambda3*consc_curr_par3_t1
consc_curr2 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
# Intercepts
consc_curr_par1_t1 ~ i1*1
consc_curr_par2_t1 ~ i2*1
consc_curr_par3_t1 ~ i3*1
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
# Unique Variances
consc_curr_par1_t1 ~~ u1*consc_curr_par1_t1
consc_curr_par2_t1 ~~ u2*consc_curr_par2_t1
consc_curr_par3_t1 ~~ u3*consc_curr_par3_t1
consc_curr_par1_t2 ~~ u1*consc_curr_par1_t2
consc_curr_par2_t2 ~~ u2*consc_curr_par2_t2
consc_curr_par3_t2 ~~ u3*consc_curr_par3_t2
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strict_consc_curr <- cfa(strict_consc_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_consc_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 219. 6528. 6614. 0.931 0.871 0.198 0.0329
2 weak 674 17 219. 6524. 6601. 0.932 0.898 0.176 0.0331
3 strong 674 15 221. 6522. 6589. 0.932 0.915 0.161 0.0339
4 strict 674 12 221. 6516. 6570. 0.933 0.933 0.143 0.0347# chi-square difference test for nested models
anova(fit_configural_consc_curr, fit_weak_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_consc_curr 8 6527.8 6613.6 218.86
fit_weak_consc_curr 10 6524.0 6600.7 219.02 0.15856 0 2
Pr(>Chisq)
fit_configural_consc_curr
fit_weak_consc_curr 0.9238anova(fit_weak_consc_curr, fit_strong_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_consc_curr 10 6524.0 6600.7 219.02
fit_strong_consc_curr 12 6521.7 6589.4 220.72 1.7006 0 2
Pr(>Chisq)
fit_weak_consc_curr
fit_strong_consc_curr 0.4273anova(fit_strong_consc_curr, fit_strict_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_consc_curr 12 6521.7 6589.4 220.72
fit_strict_consc_curr 15 6516.3 6570.5 221.34 0.62505 0 3
Pr(>Chisq)
fit_strong_consc_curr
fit_strict_consc_curr 0.8907Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + consc_ideal_par2_t1 + consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + consc_ideal_par2_t2 + consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ 1
consc_ideal_par3_t1 ~ 1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_configural_consc_ideal <- cfa(configural_consc_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_consc_ideal, fit.measures = TRUE)
# Weak invariance model
weak_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ 1
consc_ideal_par3_t1 ~ 1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_weak_consc_ideal <- cfa(weak_consc_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_consc_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ i2*1
consc_ideal_par3_t1 ~ i3*1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
# Unique Variances
consc_ideal_par1_t1 ~~ consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strong_consc_ideal <- cfa(strong_consc_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_consc_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t1 + lambda1*consc_ideal_par1_t1 + lambda2*consc_ideal_par2_t1 + lambda3*consc_ideal_par3_t1
consc_ideal2 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
# Intercepts
consc_ideal_par1_t1 ~ i1*1
consc_ideal_par2_t1 ~ i2*1
consc_ideal_par3_t1 ~ i3*1
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
# Unique Variances
consc_ideal_par1_t1 ~~ u1*consc_ideal_par1_t1
consc_ideal_par2_t1 ~~ u2*consc_ideal_par2_t1
consc_ideal_par3_t1 ~~ u3*consc_ideal_par3_t1
consc_ideal_par1_t2 ~~ u1*consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ u2*consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ u3*consc_ideal_par3_t2
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strict_consc_ideal <- cfa(strict_consc_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_consc_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 136. 5143. 5229. 0.920 0.850 0.154 0.0436
2 weak 674 17 137. 5140. 5216. 0.921 0.882 0.137 0.0472
3 strong 674 15 137. 5136. 5204. 0.922 0.902 0.125 0.0484
4 strict 674 12 138. 5131. 5185. 0.923 0.923 0.110 0.0500# chi-square difference test for nested models
anova(fit_configural_consc_ideal, fit_weak_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_consc_ideal 8 5143.4 5229.1 136.45
fit_weak_consc_ideal 10 5139.7 5216.4 136.80 0.35043 0 2
Pr(>Chisq)
fit_configural_consc_ideal
fit_weak_consc_ideal 0.8393anova(fit_weak_consc_ideal, fit_strong_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_consc_ideal 10 5139.7 5216.4 136.80
fit_strong_consc_ideal 12 5136.3 5204.0 137.43 0.62624 0 2
Pr(>Chisq)
fit_weak_consc_ideal
fit_strong_consc_ideal 0.7312anova(fit_strong_consc_ideal, fit_strict_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_consc_ideal 12 5136.3 5204 137.43
fit_strict_consc_ideal 15 5130.8 5185 137.93 0.50006 0 3
Pr(>Chisq)
fit_strong_consc_ideal
fit_strict_consc_ideal 0.9189Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + neuro_curr_par2_t1 + neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + neuro_curr_par2_t2 + neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ 1
neuro_curr_par3_t1 ~ 1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_configural_neuro_curr <- cfa(configural_neuro_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_neuro_curr, fit.measures = TRUE)
# Weak invariance model
weak_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ 1
neuro_curr_par3_t1 ~ 1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_weak_neuro_curr <- cfa(weak_neuro_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_neuro_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ i2*1
neuro_curr_par3_t1 ~ i3*1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
# Unique Variances
neuro_curr_par1_t1 ~~ neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strong_neuro_curr <- cfa(strong_neuro_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_neuro_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t1 + lambda1*neuro_curr_par1_t1 + lambda2*neuro_curr_par2_t1 + lambda3*neuro_curr_par3_t1
neuro_curr2 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
# Intercepts
neuro_curr_par1_t1 ~ i1*1
neuro_curr_par2_t1 ~ i2*1
neuro_curr_par3_t1 ~ i3*1
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
# Unique Variances
neuro_curr_par1_t1 ~~ u1*neuro_curr_par1_t1
neuro_curr_par2_t1 ~~ u2*neuro_curr_par2_t1
neuro_curr_par3_t1 ~~ u3*neuro_curr_par3_t1
neuro_curr_par1_t2 ~~ u1*neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ u2*neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ u3*neuro_curr_par3_t2
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strict_neuro_curr <- cfa(strict_neuro_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_neuro_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 173. 6934. 7020. 0.949 0.905 0.175 0.0265
2 weak 674 17 173. 6930. 7007. 0.950 0.925 0.156 0.0271
3 strong 674 15 175. 6928. 6996. 0.950 0.937 0.142 0.0268
4 strict 674 12 180. 6927. 6981. 0.949 0.949 0.128 0.0296# chi-square difference test for nested models
anova(fit_configural_neuro_curr, fit_weak_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_neuro_curr 8 6934.0 7019.8 173.01
fit_weak_neuro_curr 10 6930.3 7007.1 173.32 0.30625 0 2
Pr(>Chisq)
fit_configural_neuro_curr
fit_weak_neuro_curr 0.858anova(fit_weak_neuro_curr, fit_strong_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_neuro_curr 10 6930.3 7007.1 173.32
fit_strong_neuro_curr 12 6928.4 6996.1 175.36 2.0421 0.0055866 2
Pr(>Chisq)
fit_weak_neuro_curr
fit_strong_neuro_curr 0.3602anova(fit_strong_neuro_curr, fit_strict_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_neuro_curr 12 6928.4 6996.1 175.36
fit_strict_neuro_curr 15 6926.9 6981.1 179.87 4.5075 0.027305 3
Pr(>Chisq)
fit_strong_neuro_curr
fit_strict_neuro_curr 0.2116Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + neuro_ideal_par2_t1 + neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + neuro_ideal_par2_t2 + neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ 1
neuro_ideal_par3_t1 ~ 1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_configural_neuro_ideal <- cfa(configural_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_neuro_ideal, fit.measures = TRUE)
# Weak invariance model
weak_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ 1
neuro_ideal_par3_t1 ~ 1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_weak_neuro_ideal <- cfa(weak_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_neuro_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ i2*1
neuro_ideal_par3_t1 ~ i3*1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
# Unique Variances
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strong_neuro_ideal <- cfa(strong_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_neuro_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t1 + lambda1*neuro_ideal_par1_t1 + lambda2*neuro_ideal_par2_t1 + lambda3*neuro_ideal_par3_t1
neuro_ideal2 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
# Intercepts
neuro_ideal_par1_t1 ~ i1*1
neuro_ideal_par2_t1 ~ i2*1
neuro_ideal_par3_t1 ~ i3*1
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
# Unique Variances
neuro_ideal_par1_t1 ~~ u1*neuro_ideal_par1_t1
neuro_ideal_par2_t1 ~~ u2*neuro_ideal_par2_t1
neuro_ideal_par3_t1 ~~ u3*neuro_ideal_par3_t1
neuro_ideal_par1_t2 ~~ u1*neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ u2*neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ u3*neuro_ideal_par3_t2
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strict_neuro_ideal <- cfa(strict_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_neuro_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 15.5 5246. 5332. 0.995 0.991 0.0374 0.0129
2 weak 674 17 22.5 5249. 5326. 0.992 0.988 0.0431 0.0331
3 strong 674 15 24.8 5248. 5315. 0.992 0.989 0.0398 0.0369
4 strict 674 12 26.3 5243. 5297. 0.993 0.993 0.0335 0.0296# chi-square difference test for nested models
anova(fit_configural_neuro_ideal, fit_weak_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_neuro_ideal 8 5246.4 5332.1 15.549
fit_weak_neuro_ideal 10 5249.4 5326.1 22.520 6.9709 0.060725 2
Pr(>Chisq)
fit_configural_neuro_ideal
fit_weak_neuro_ideal 0.03064 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_weak_neuro_ideal, fit_strong_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_neuro_ideal 10 5249.4 5326.1 22.52
fit_strong_neuro_ideal 12 5247.7 5315.4 24.81 2.2898 0.014663 2
Pr(>Chisq)
fit_weak_neuro_ideal
fit_strong_neuro_ideal 0.3183anova(fit_strong_neuro_ideal, fit_strict_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_neuro_ideal 12 5247.7 5315.4 24.810
fit_strict_neuro_ideal 15 5243.2 5297.3 26.319 1.5092 0 3
Pr(>Chisq)
fit_strong_neuro_ideal
fit_strict_neuro_ideal 0.6801Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + openn_curr_par2_t1 + openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + openn_curr_par2_t2 + openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ 1
openn_curr_par3_t1 ~ 1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_configural_openn_curr <- cfa(configural_openn_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_openn_curr, fit.measures = TRUE)
# Weak invariance model
weak_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ 1
openn_curr_par3_t1 ~ 1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_weak_openn_curr <- cfa(weak_openn_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_openn_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ i2*1
openn_curr_par3_t1 ~ i3*1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
# Unique Variances
openn_curr_par1_t1 ~~ openn_curr_par1_t1
openn_curr_par2_t1 ~~ openn_curr_par2_t1
openn_curr_par3_t1 ~~ openn_curr_par3_t1
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strong_openn_curr <- cfa(strong_openn_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_openn_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t1 + lambda1*openn_curr_par1_t1 + lambda2*openn_curr_par2_t1 + lambda3*openn_curr_par3_t1
openn_curr2 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
# Intercepts
openn_curr_par1_t1 ~ i1*1
openn_curr_par2_t1 ~ i2*1
openn_curr_par3_t1 ~ i3*1
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
# Unique Variances
openn_curr_par1_t1 ~~ u1*openn_curr_par1_t1
openn_curr_par2_t1 ~~ u2*openn_curr_par2_t1
openn_curr_par3_t1 ~~ u3*openn_curr_par3_t1
openn_curr_par1_t2 ~~ u1*openn_curr_par1_t2
openn_curr_par2_t2 ~~ u2*openn_curr_par2_t2
openn_curr_par3_t2 ~~ u3*openn_curr_par3_t2
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strict_openn_curr <- cfa(strict_openn_curr, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_openn_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 155. 6334. 6420. 0.935 0.878 0.165 0.0370
2 weak 674 17 156. 6330. 6407. 0.936 0.903 0.147 0.0382
3 strong 674 15 160. 6330. 6398. 0.935 0.918 0.135 0.0396
4 strict 674 12 166. 6330. 6384. 0.933 0.933 0.122 0.0421# chi-square difference test for nested models
anova(fit_configural_openn_curr, fit_weak_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_openn_curr 8 6333.8 6419.6 155.48
fit_weak_openn_curr 10 6330.1 6406.8 155.75 0.26746 0 2
Pr(>Chisq)
fit_configural_openn_curr
fit_weak_openn_curr 0.8748anova(fit_weak_openn_curr, fit_strong_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_openn_curr 10 6330.1 6406.8 155.75
fit_strong_openn_curr 12 6330.0 6397.7 159.66 3.914 0.037681 2
Pr(>Chisq)
fit_weak_openn_curr
fit_strong_openn_curr 0.1413anova(fit_strong_openn_curr, fit_strict_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_openn_curr 12 6330 6397.7 159.66
fit_strict_openn_curr 15 6330 6384.2 165.68 6.0204 0.038649 3
Pr(>Chisq)
fit_strong_openn_curr
fit_strict_openn_curr 0.1106Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + openn_ideal_par2_t1 + openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + openn_ideal_par2_t2 + openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ 1
openn_ideal_par3_t1 ~ 1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_configural_openn_ideal <- cfa(configural_openn_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural_openn_ideal, fit.measures = TRUE)
# Weak invariance model
weak_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ 1
openn_ideal_par3_t1 ~ 1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_weak_openn_ideal <- cfa(weak_openn_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak_openn_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ i2*1
openn_ideal_par3_t1 ~ i3*1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
# Unique Variances
openn_ideal_par1_t1 ~~ openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strong_openn_ideal <- cfa(strong_openn_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong_openn_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t1 + lambda1*openn_ideal_par1_t1 + lambda2*openn_ideal_par2_t1 + lambda3*openn_ideal_par3_t1
openn_ideal2 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
# Intercepts
openn_ideal_par1_t1 ~ i1*1
openn_ideal_par2_t1 ~ i2*1
openn_ideal_par3_t1 ~ i3*1
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
# Unique Variances
openn_ideal_par1_t1 ~~ u1*openn_ideal_par1_t1
openn_ideal_par2_t1 ~~ u2*openn_ideal_par2_t1
openn_ideal_par3_t1 ~~ u3*openn_ideal_par3_t1
openn_ideal_par1_t2 ~~ u1*openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ u2*openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ u3*openn_ideal_par3_t2
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strict_openn_ideal <- cfa(strict_openn_ideal, data = df_sbsa2_wide_pers %>% filter(rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict_openn_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 674 19 162. 6090. 6176. 0.886 0.786 0.169 0.0648
2 weak 674 17 168. 6092. 6169. 0.883 0.824 0.153 0.0844
3 strong 674 15 173. 6093. 6161. 0.880 0.851 0.141 0.0808
4 strict 674 12 182. 6096. 6150. 0.876 0.876 0.128 0.0711# chi-square difference test for nested models
anova(fit_configural_openn_ideal, fit_weak_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural_openn_ideal 8 6090.1 6175.9 162.04
fit_weak_openn_ideal 10 6092.1 6168.8 168.04 5.9987 0.054465 2
Pr(>Chisq)
fit_configural_openn_ideal
fit_weak_openn_ideal 0.04982 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_weak_openn_ideal, fit_strong_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak_openn_ideal 10 6092.1 6168.8 168.04
fit_strong_openn_ideal 12 6093.1 6160.8 173.05 5.0096 0.047251 2
Pr(>Chisq)
fit_weak_openn_ideal
fit_strong_openn_ideal 0.08169 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit_strong_openn_ideal, fit_strict_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong_openn_ideal 12 6093.1 6160.8 173.05
fit_strict_openn_ideal 15 6095.8 6150.0 181.78 8.7264 0.053217 3
Pr(>Chisq)
fit_strong_openn_ideal
fit_strict_openn_ideal 0.03316 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Testing for measurement invariance of the latent BFI-2 traits across time, from T2 to T3.
Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + extra_curr_par2_t2 + extra_curr_par3_t2
extra_curr2 =~ NA*extra_curr_par1_t3 + lambda1*extra_curr_par1_t3 + extra_curr_par2_t3 + extra_curr_par3_t3
# Intercepts
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
extra_curr_par1_t3 ~ i1*1
extra_curr_par2_t3 ~ 1
extra_curr_par3_t3 ~ 1
# Unique Variances
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
extra_curr_par1_t3 ~~ extra_curr_par1_t3
extra_curr_par2_t3 ~~ extra_curr_par2_t3
extra_curr_par3_t3 ~~ extra_curr_par3_t3
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_configural2_extra_curr <- cfa(configural2_extra_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_extra_curr, fit.measures = TRUE)
# Weak invariance model
weak2_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
extra_curr2 =~ NA*extra_curr_par1_t3 + lambda1*extra_curr_par1_t3 + lambda2*extra_curr_par2_t3 + lambda3*extra_curr_par3_t3
# Intercepts
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ 1
extra_curr_par3_t2 ~ 1
extra_curr_par1_t3 ~ i1*1
extra_curr_par2_t3 ~ 1
extra_curr_par3_t3 ~ 1
# Unique Variances
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
extra_curr_par1_t3 ~~ extra_curr_par1_t3
extra_curr_par2_t3 ~~ extra_curr_par2_t3
extra_curr_par3_t3 ~~ extra_curr_par3_t3
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_weak2_extra_curr <- cfa(weak2_extra_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_extra_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
extra_curr2 =~ NA*extra_curr_par1_t3 + lambda1*extra_curr_par1_t3 + lambda2*extra_curr_par2_t3 + lambda3*extra_curr_par3_t3
# Intercepts
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
extra_curr_par1_t3 ~ i1*1
extra_curr_par2_t3 ~ i2*1
extra_curr_par3_t3 ~ i3*1
# Unique Variances
extra_curr_par1_t2 ~~ extra_curr_par1_t2
extra_curr_par2_t2 ~~ extra_curr_par2_t2
extra_curr_par3_t2 ~~ extra_curr_par3_t2
extra_curr_par1_t3 ~~ extra_curr_par1_t3
extra_curr_par2_t3 ~~ extra_curr_par2_t3
extra_curr_par3_t3 ~~ extra_curr_par3_t3
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strong2_extra_curr <- cfa(strong2_extra_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_extra_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_extra_curr <- '
# Define the latent factors
extra_curr1 =~ NA*extra_curr_par1_t2 + lambda1*extra_curr_par1_t2 + lambda2*extra_curr_par2_t2 + lambda3*extra_curr_par3_t2
extra_curr2 =~ NA*extra_curr_par1_t3 + lambda1*extra_curr_par1_t3 + lambda2*extra_curr_par2_t3 + lambda3*extra_curr_par3_t3
# Intercepts
extra_curr_par1_t2 ~ i1*1
extra_curr_par2_t2 ~ i2*1
extra_curr_par3_t2 ~ i3*1
extra_curr_par1_t3 ~ i1*1
extra_curr_par2_t3 ~ i2*1
extra_curr_par3_t3 ~ i3*1
# Unique Variances
extra_curr_par1_t2 ~~ u1*extra_curr_par1_t2
extra_curr_par2_t2 ~~ u2*extra_curr_par2_t2
extra_curr_par3_t2 ~~ u3*extra_curr_par3_t2
extra_curr_par1_t3 ~~ u1*extra_curr_par1_t3
extra_curr_par2_t3 ~~ u2*extra_curr_par2_t3
extra_curr_par3_t3 ~~ u3*extra_curr_par3_t3
# Latent Variable Means
extra_curr1 ~ 0*1
extra_curr2 ~ 1
# Latent Variable Variances and Covariance
extra_curr1 ~~ 1*extra_curr1
extra_curr2 ~~ extra_curr2
extra_curr1 ~~ extra_curr2
'
fit_strict2_extra_curr <- cfa(strict2_extra_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_extra_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_extra_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 398. 6335. 6418. 0.808 0.640 0.291 0.0696
2 weak 577 17 399. 6333. 6407. 0.808 0.713 0.260 0.0747
3 strong 577 15 402. 6332. 6397. 0.808 0.760 0.237 0.0726
4 strict 577 12 403. 6327. 6379. 0.809 0.809 0.212 0.0731# chi-square difference test for nested models
anova(fit_configural2_extra_curr, fit_weak2_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_extra_curr 8 6335.3 6418.1 397.63
fit_weak2_extra_curr 10 6333.0 6407.1 399.35 1.7174 0 2
Pr(>Chisq)
fit_configural2_extra_curr
fit_weak2_extra_curr 0.4237anova(fit_weak2_extra_curr, fit_strong2_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_extra_curr 10 6333.0 6407.1 399.35
fit_strong2_extra_curr 12 6331.8 6397.2 402.16 2.8056 0.026421 2
Pr(>Chisq)
fit_weak2_extra_curr
fit_strong2_extra_curr 0.2459anova(fit_strong2_extra_curr, fit_strict2_extra_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_extra_curr 12 6331.8 6397.2 402.16
fit_strict2_extra_curr 15 6326.7 6379.0 403.07 0.91245 0 3
Pr(>Chisq)
fit_strong2_extra_curr
fit_strict2_extra_curr 0.8224Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + extra_ideal_par2_t2 + extra_ideal_par3_t2
extra_ideal2 =~ NA*extra_ideal_par1_t3 + lambda1*extra_ideal_par1_t3 + extra_ideal_par2_t3 + extra_ideal_par3_t3
# Intercepts
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
extra_ideal_par1_t3 ~ i1*1
extra_ideal_par2_t3 ~ 1
extra_ideal_par3_t3 ~ 1
# Unique Variances
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
extra_ideal_par1_t3 ~~ extra_ideal_par1_t3
extra_ideal_par2_t3 ~~ extra_ideal_par2_t3
extra_ideal_par3_t3 ~~ extra_ideal_par3_t3
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_configural2_extra_ideal <- cfa(configural2_extra_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_extra_ideal, fit.measures = TRUE)
# Weak invariance model
weak2_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
extra_ideal2 =~ NA*extra_ideal_par1_t3 + lambda1*extra_ideal_par1_t3 + lambda2*extra_ideal_par2_t3 + lambda3*extra_ideal_par3_t3
# Intercepts
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ 1
extra_ideal_par3_t2 ~ 1
extra_ideal_par1_t3 ~ i1*1
extra_ideal_par2_t3 ~ 1
extra_ideal_par3_t3 ~ 1
# Unique Variances
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
extra_ideal_par1_t3 ~~ extra_ideal_par1_t3
extra_ideal_par2_t3 ~~ extra_ideal_par2_t3
extra_ideal_par3_t3 ~~ extra_ideal_par3_t3
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_weak2_extra_ideal <- cfa(weak2_extra_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_extra_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
extra_ideal2 =~ NA*extra_ideal_par1_t3 + lambda1*extra_ideal_par1_t3 + lambda2*extra_ideal_par2_t3 + lambda3*extra_ideal_par3_t3
# Intercepts
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
extra_ideal_par1_t3 ~ i1*1
extra_ideal_par2_t3 ~ i2*1
extra_ideal_par3_t3 ~ i3*1
# Unique Variances
extra_ideal_par1_t2 ~~ extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ extra_ideal_par3_t2
extra_ideal_par1_t3 ~~ extra_ideal_par1_t3
extra_ideal_par2_t3 ~~ extra_ideal_par2_t3
extra_ideal_par3_t3 ~~ extra_ideal_par3_t3
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strong2_extra_ideal <- cfa(strong2_extra_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_extra_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_extra_ideal <- '
# Define the latent factors
extra_ideal1 =~ NA*extra_ideal_par1_t2 + lambda1*extra_ideal_par1_t2 + lambda2*extra_ideal_par2_t2 + lambda3*extra_ideal_par3_t2
extra_ideal2 =~ NA*extra_ideal_par1_t3 + lambda1*extra_ideal_par1_t3 + lambda2*extra_ideal_par2_t3 + lambda3*extra_ideal_par3_t3
# Intercepts
extra_ideal_par1_t2 ~ i1*1
extra_ideal_par2_t2 ~ i2*1
extra_ideal_par3_t2 ~ i3*1
extra_ideal_par1_t3 ~ i1*1
extra_ideal_par2_t3 ~ i2*1
extra_ideal_par3_t3 ~ i3*1
# Unique Variances
extra_ideal_par1_t2 ~~ u1*extra_ideal_par1_t2
extra_ideal_par2_t2 ~~ u2*extra_ideal_par2_t2
extra_ideal_par3_t2 ~~ u3*extra_ideal_par3_t2
extra_ideal_par1_t3 ~~ u1*extra_ideal_par1_t3
extra_ideal_par2_t3 ~~ u2*extra_ideal_par2_t3
extra_ideal_par3_t3 ~~ u3*extra_ideal_par3_t3
# Latent Variable Means
extra_ideal1 ~ 0*1
extra_ideal2 ~ 1
# Latent Variable Variances and Covariance
extra_ideal1 ~~ 1*extra_ideal1
extra_ideal2 ~~ extra_ideal2
extra_ideal1 ~~ extra_ideal2
'
fit_strict2_extra_ideal <- cfa(strict2_extra_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_extra_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_extra_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 169. 5357. 5440. 0.844 0.707 0.187 0.0617
2 weak 577 17 170. 5353. 5427. 0.845 0.768 0.166 0.0639
3 strong 577 15 170. 5350. 5415. 0.847 0.808 0.151 0.0637
4 strict 577 12 172. 5346. 5398. 0.847 0.847 0.135 0.0675# chi-square difference test for nested models
anova(fit_configural2_extra_ideal, fit_weak2_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_extra_ideal 8 5356.9 5439.7 169.37
fit_weak2_extra_ideal 10 5353.3 5427.4 169.75 0.38629 0 2
Pr(>Chisq)
fit_configural2_extra_ideal
fit_weak2_extra_ideal 0.8244anova(fit_weak2_extra_ideal, fit_strong2_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_extra_ideal 10 5353.3 5427.4 169.75
fit_strong2_extra_ideal 12 5349.9 5415.2 170.34 0.5878 0 2
Pr(>Chisq)
fit_weak2_extra_ideal
fit_strong2_extra_ideal 0.7454anova(fit_strong2_extra_ideal, fit_strict2_extra_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_extra_ideal 12 5349.9 5415.2 170.34
fit_strict2_extra_ideal 15 5345.9 5398.2 172.39 2.0524 0 3
Pr(>Chisq)
fit_strong2_extra_ideal
fit_strict2_extra_ideal 0.5616Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + agree_curr_par2_t2 + agree_curr_par3_t2
agree_curr2 =~ NA*agree_curr_par1_t3 + lambda1*agree_curr_par1_t3 + agree_curr_par2_t3 + agree_curr_par3_t3
# Intercepts
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
agree_curr_par1_t3 ~ i1*1
agree_curr_par2_t3 ~ 1
agree_curr_par3_t3 ~ 1
# Unique Variances
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
agree_curr_par1_t3 ~~ agree_curr_par1_t3
agree_curr_par2_t3 ~~ agree_curr_par2_t3
agree_curr_par3_t3 ~~ agree_curr_par3_t3
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_configural2_agree_curr <- cfa(configural2_agree_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_agree_curr, fit.measures = TRUE)
# Weak invariance model
weak2_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
agree_curr2 =~ NA*agree_curr_par1_t3 + lambda1*agree_curr_par1_t3 + lambda2*agree_curr_par2_t3 + lambda3*agree_curr_par3_t3
# Intercepts
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ 1
agree_curr_par3_t2 ~ 1
agree_curr_par1_t3 ~ i1*1
agree_curr_par2_t3 ~ 1
agree_curr_par3_t3 ~ 1
# Unique Variances
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
agree_curr_par1_t3 ~~ agree_curr_par1_t3
agree_curr_par2_t3 ~~ agree_curr_par2_t3
agree_curr_par3_t3 ~~ agree_curr_par3_t3
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_weak2_agree_curr <- cfa(weak2_agree_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_agree_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
agree_curr2 =~ NA*agree_curr_par1_t3 + lambda1*agree_curr_par1_t3 + lambda2*agree_curr_par2_t3 + lambda3*agree_curr_par3_t3
# Intercepts
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
agree_curr_par1_t3 ~ i1*1
agree_curr_par2_t3 ~ i2*1
agree_curr_par3_t3 ~ i3*1
# Unique Variances
agree_curr_par1_t2 ~~ agree_curr_par1_t2
agree_curr_par2_t2 ~~ agree_curr_par2_t2
agree_curr_par3_t2 ~~ agree_curr_par3_t2
agree_curr_par1_t3 ~~ agree_curr_par1_t3
agree_curr_par2_t3 ~~ agree_curr_par2_t3
agree_curr_par3_t3 ~~ agree_curr_par3_t3
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strong2_agree_curr <- cfa(strong2_agree_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_agree_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_agree_curr <- '
# Define the latent factors
agree_curr1 =~ NA*agree_curr_par1_t2 + lambda1*agree_curr_par1_t2 + lambda2*agree_curr_par2_t2 + lambda3*agree_curr_par3_t2
agree_curr2 =~ NA*agree_curr_par1_t3 + lambda1*agree_curr_par1_t3 + lambda2*agree_curr_par2_t3 + lambda3*agree_curr_par3_t3
# Intercepts
agree_curr_par1_t2 ~ i1*1
agree_curr_par2_t2 ~ i2*1
agree_curr_par3_t2 ~ i3*1
agree_curr_par1_t3 ~ i1*1
agree_curr_par2_t3 ~ i2*1
agree_curr_par3_t3 ~ i3*1
# Unique Variances
agree_curr_par1_t2 ~~ u1*agree_curr_par1_t2
agree_curr_par2_t2 ~~ u2*agree_curr_par2_t2
agree_curr_par3_t2 ~~ u3*agree_curr_par3_t2
agree_curr_par1_t3 ~~ u1*agree_curr_par1_t3
agree_curr_par2_t3 ~~ u2*agree_curr_par2_t3
agree_curr_par3_t3 ~~ u3*agree_curr_par3_t3
# Latent Variable Means
agree_curr1 ~ 0*1
agree_curr2 ~ 1
# Latent Variable Variances and Covariance
agree_curr1 ~~ 1*agree_curr1
agree_curr2 ~~ agree_curr2
agree_curr1 ~~ agree_curr2
'
fit_strict2_agree_curr <- cfa(strict2_agree_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_agree_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_agree_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 239. 5005. 5088. 0.839 0.698 0.224 0.0754
2 weak 577 17 240. 5002. 5076. 0.840 0.760 0.200 0.0764
3 strong 577 15 240. 4998. 5063. 0.841 0.802 0.181 0.0767
4 strict 577 12 242. 4994. 5046. 0.842 0.842 0.162 0.0822# chi-square difference test for nested models
anova(fit_configural2_agree_curr, fit_weak2_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_agree_curr 8 5005.3 5088.1 239.49
fit_weak2_agree_curr 10 5001.6 5075.7 239.74 0.24587 0 2
Pr(>Chisq)
fit_configural2_agree_curr
fit_weak2_agree_curr 0.8843anova(fit_weak2_agree_curr, fit_strong2_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_agree_curr 10 5001.6 5075.7 239.74
fit_strong2_agree_curr 12 4997.7 5063.1 239.83 0.093468 0 2
Pr(>Chisq)
fit_weak2_agree_curr
fit_strong2_agree_curr 0.9543anova(fit_strong2_agree_curr, fit_strict2_agree_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_agree_curr 12 4997.7 5063.1 239.83
fit_strict2_agree_curr 15 4993.8 5046.1 241.97 2.1344 0 3
Pr(>Chisq)
fit_strong2_agree_curr
fit_strict2_agree_curr 0.545Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + agree_ideal_par2_t2 + agree_ideal_par3_t2
agree_ideal2 =~ NA*agree_ideal_par1_t3 + lambda1*agree_ideal_par1_t3 + agree_ideal_par2_t3 + agree_ideal_par3_t3
# Intercepts
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
agree_ideal_par1_t3 ~ i1*1
agree_ideal_par2_t3 ~ 1
agree_ideal_par3_t3 ~ 1
# Unique Variances
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
agree_ideal_par1_t3 ~~ agree_ideal_par1_t3
agree_ideal_par2_t3 ~~ agree_ideal_par2_t3
agree_ideal_par3_t3 ~~ agree_ideal_par3_t3
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_configural2_agree_ideal <- cfa(configural2_agree_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_agree_ideal, fit.measures = TRUE)
# Weak invariance model
weak2_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
agree_ideal2 =~ NA*agree_ideal_par1_t3 + lambda1*agree_ideal_par1_t3 + lambda2*agree_ideal_par2_t3 + lambda3*agree_ideal_par3_t3
# Intercepts
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ 1
agree_ideal_par3_t2 ~ 1
agree_ideal_par1_t3 ~ i1*1
agree_ideal_par2_t3 ~ 1
agree_ideal_par3_t3 ~ 1
# Unique Variances
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
agree_ideal_par1_t3 ~~ agree_ideal_par1_t3
agree_ideal_par2_t3 ~~ agree_ideal_par2_t3
agree_ideal_par3_t3 ~~ agree_ideal_par3_t3
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_weak2_agree_ideal <- cfa(weak2_agree_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_agree_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
agree_ideal2 =~ NA*agree_ideal_par1_t3 + lambda1*agree_ideal_par1_t3 + lambda2*agree_ideal_par2_t3 + lambda3*agree_ideal_par3_t3
# Intercepts
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
agree_ideal_par1_t3 ~ i1*1
agree_ideal_par2_t3 ~ i2*1
agree_ideal_par3_t3 ~ i3*1
# Unique Variances
agree_ideal_par1_t2 ~~ agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ agree_ideal_par3_t2
agree_ideal_par1_t3 ~~ agree_ideal_par1_t3
agree_ideal_par2_t3 ~~ agree_ideal_par2_t3
agree_ideal_par3_t3 ~~ agree_ideal_par3_t3
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strong2_agree_ideal <- cfa(strong2_agree_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_agree_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_agree_ideal <- '
# Define the latent factors
agree_ideal1 =~ NA*agree_ideal_par1_t2 + lambda1*agree_ideal_par1_t2 + lambda2*agree_ideal_par2_t2 + lambda3*agree_ideal_par3_t2
agree_ideal2 =~ NA*agree_ideal_par1_t3 + lambda1*agree_ideal_par1_t3 + lambda2*agree_ideal_par2_t3 + lambda3*agree_ideal_par3_t3
# Intercepts
agree_ideal_par1_t2 ~ i1*1
agree_ideal_par2_t2 ~ i2*1
agree_ideal_par3_t2 ~ i3*1
agree_ideal_par1_t3 ~ i1*1
agree_ideal_par2_t3 ~ i2*1
agree_ideal_par3_t3 ~ i3*1
# Unique Variances
agree_ideal_par1_t2 ~~ u1*agree_ideal_par1_t2
agree_ideal_par2_t2 ~~ u2*agree_ideal_par2_t2
agree_ideal_par3_t2 ~~ u3*agree_ideal_par3_t2
agree_ideal_par1_t3 ~~ u1*agree_ideal_par1_t3
agree_ideal_par2_t3 ~~ u2*agree_ideal_par2_t3
agree_ideal_par3_t3 ~~ u3*agree_ideal_par3_t3
# Latent Variable Means
agree_ideal1 ~ 0*1
agree_ideal2 ~ 1
# Latent Variable Variances and Covariance
agree_ideal1 ~~ 1*agree_ideal1
agree_ideal2 ~~ agree_ideal2
agree_ideal1 ~~ agree_ideal2
'
fit_strict2_agree_ideal <- cfa(strict2_agree_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_agree_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_agree_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 183. 4928. 5011. 0.891 0.795 0.195 0.0537
2 weak 577 17 186. 4927. 5001. 0.890 0.835 0.175 0.0717
3 strong 577 15 188. 4924. 4990. 0.891 0.863 0.159 0.0724
4 strict 577 12 189. 4919. 4972. 0.892 0.892 0.142 0.0654# chi-square difference test for nested models
anova(fit_configural2_agree_ideal, fit_weak2_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_agree_ideal 8 4928.0 5010.8 183.39
fit_weak2_agree_ideal 10 4927.1 5001.2 186.45 3.0622 0.030339 2
Pr(>Chisq)
fit_configural2_agree_ideal
fit_weak2_agree_ideal 0.2163anova(fit_weak2_agree_ideal, fit_strong2_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_agree_ideal 10 4927.1 5001.2 186.45
fit_strong2_agree_ideal 12 4924.5 4989.8 187.84 1.3937 0 2
Pr(>Chisq)
fit_weak2_agree_ideal
fit_strong2_agree_ideal 0.4982anova(fit_strong2_agree_ideal, fit_strict2_agree_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_agree_ideal 12 4924.5 4989.8 187.84
fit_strict2_agree_ideal 15 4919.5 4971.7 188.82 0.97691 0 3
Pr(>Chisq)
fit_strong2_agree_ideal
fit_strict2_agree_ideal 0.8068Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + consc_curr_par2_t2 + consc_curr_par3_t2
consc_curr2 =~ NA*consc_curr_par1_t3 + lambda1*consc_curr_par1_t3 + consc_curr_par2_t3 + consc_curr_par3_t3
# Intercepts
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
consc_curr_par1_t3 ~ i1*1
consc_curr_par2_t3 ~ 1
consc_curr_par3_t3 ~ 1
# Unique Variances
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
consc_curr_par1_t3 ~~ consc_curr_par1_t3
consc_curr_par2_t3 ~~ consc_curr_par2_t3
consc_curr_par3_t3 ~~ consc_curr_par3_t3
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_configural2_consc_curr <- cfa(configural2_consc_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_consc_curr, fit.measures = TRUE)
# Weak invariance model
weak2_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
consc_curr2 =~ NA*consc_curr_par1_t3 + lambda1*consc_curr_par1_t3 + lambda2*consc_curr_par2_t3 + lambda3*consc_curr_par3_t3
# Intercepts
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ 1
consc_curr_par3_t2 ~ 1
consc_curr_par1_t3 ~ i1*1
consc_curr_par2_t3 ~ 1
consc_curr_par3_t3 ~ 1
# Unique Variances
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
consc_curr_par1_t3 ~~ consc_curr_par1_t3
consc_curr_par2_t3 ~~ consc_curr_par2_t3
consc_curr_par3_t3 ~~ consc_curr_par3_t3
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_weak2_consc_curr <- cfa(weak2_consc_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_consc_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
consc_curr2 =~ NA*consc_curr_par1_t3 + lambda1*consc_curr_par1_t3 + lambda2*consc_curr_par2_t3 + lambda3*consc_curr_par3_t3
# Intercepts
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
consc_curr_par1_t3 ~ i1*1
consc_curr_par2_t3 ~ i2*1
consc_curr_par3_t3 ~ i3*1
# Unique Variances
consc_curr_par1_t2 ~~ consc_curr_par1_t2
consc_curr_par2_t2 ~~ consc_curr_par2_t2
consc_curr_par3_t2 ~~ consc_curr_par3_t2
consc_curr_par1_t3 ~~ consc_curr_par1_t3
consc_curr_par2_t3 ~~ consc_curr_par2_t3
consc_curr_par3_t3 ~~ consc_curr_par3_t3
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strong2_consc_curr <- cfa(strong2_consc_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_consc_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_consc_curr <- '
# Define the latent factors
consc_curr1 =~ NA*consc_curr_par1_t2 + lambda1*consc_curr_par1_t2 + lambda2*consc_curr_par2_t2 + lambda3*consc_curr_par3_t2
consc_curr2 =~ NA*consc_curr_par1_t3 + lambda1*consc_curr_par1_t3 + lambda2*consc_curr_par2_t3 + lambda3*consc_curr_par3_t3
# Intercepts
consc_curr_par1_t2 ~ i1*1
consc_curr_par2_t2 ~ i2*1
consc_curr_par3_t2 ~ i3*1
consc_curr_par1_t3 ~ i1*1
consc_curr_par2_t3 ~ i2*1
consc_curr_par3_t3 ~ i3*1
# Unique Variances
consc_curr_par1_t2 ~~ u1*consc_curr_par1_t2
consc_curr_par2_t2 ~~ u2*consc_curr_par2_t2
consc_curr_par3_t2 ~~ u3*consc_curr_par3_t2
consc_curr_par1_t3 ~~ u1*consc_curr_par1_t3
consc_curr_par2_t3 ~~ u2*consc_curr_par2_t3
consc_curr_par3_t3 ~~ u3*consc_curr_par3_t3
# Latent Variable Means
consc_curr1 ~ 0*1
consc_curr2 ~ 1
# Latent Variable Variances and Covariance
consc_curr1 ~~ 1*consc_curr1
consc_curr2 ~~ consc_curr2
consc_curr1 ~~ consc_curr2
'
fit_strict2_consc_curr <- cfa(strict2_consc_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_consc_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_consc_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 173. 5578. 5661. 0.940 0.888 0.189 0.0312
2 weak 577 17 173. 5575. 5649. 0.941 0.912 0.168 0.0317
3 strong 577 15 174. 5571. 5637. 0.942 0.927 0.153 0.0314
4 strict 577 12 174. 5566. 5618. 0.942 0.942 0.136 0.0317# chi-square difference test for nested models
anova(fit_configural2_consc_curr, fit_weak2_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_consc_curr 8 5578.5 5661.3 173.11
fit_weak2_consc_curr 10 5574.6 5648.7 173.29 0.1738 0 2
Pr(>Chisq)
fit_configural2_consc_curr
fit_weak2_consc_curr 0.9168anova(fit_weak2_consc_curr, fit_strong2_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_consc_curr 10 5574.6 5648.7 173.29
fit_strong2_consc_curr 12 5571.1 5636.5 173.79 0.50808 0 2
Pr(>Chisq)
fit_weak2_consc_curr
fit_strong2_consc_curr 0.7757anova(fit_strong2_consc_curr, fit_strict2_consc_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_consc_curr 12 5571.1 5636.5 173.79
fit_strict2_consc_curr 15 5565.8 5618.1 174.44 0.64404 0 3
Pr(>Chisq)
fit_strong2_consc_curr
fit_strict2_consc_curr 0.8863Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + consc_ideal_par2_t2 + consc_ideal_par3_t2
consc_ideal2 =~ NA*consc_ideal_par1_t3 + lambda1*consc_ideal_par1_t3 + consc_ideal_par2_t3 + consc_ideal_par3_t3
# Intercepts
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
consc_ideal_par1_t3 ~ i1*1
consc_ideal_par2_t3 ~ 1
consc_ideal_par3_t3 ~ 1
# Unique Variances
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
consc_ideal_par1_t3 ~~ consc_ideal_par1_t3
consc_ideal_par2_t3 ~~ consc_ideal_par2_t3
consc_ideal_par3_t3 ~~ consc_ideal_par3_t3
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_configural2_consc_ideal <- cfa(configural2_consc_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_consc_ideal, fit.measures = TRUE)
# Weak invariance model
weak2_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
consc_ideal2 =~ NA*consc_ideal_par1_t3 + lambda1*consc_ideal_par1_t3 + lambda2*consc_ideal_par2_t3 + lambda3*consc_ideal_par3_t3
# Intercepts
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ 1
consc_ideal_par3_t2 ~ 1
consc_ideal_par1_t3 ~ i1*1
consc_ideal_par2_t3 ~ 1
consc_ideal_par3_t3 ~ 1
# Unique Variances
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
consc_ideal_par1_t3 ~~ consc_ideal_par1_t3
consc_ideal_par2_t3 ~~ consc_ideal_par2_t3
consc_ideal_par3_t3 ~~ consc_ideal_par3_t3
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_weak2_consc_ideal <- cfa(weak2_consc_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_consc_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
consc_ideal2 =~ NA*consc_ideal_par1_t3 + lambda1*consc_ideal_par1_t3 + lambda2*consc_ideal_par2_t3 + lambda3*consc_ideal_par3_t3
# Intercepts
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
consc_ideal_par1_t3 ~ i1*1
consc_ideal_par2_t3 ~ i2*1
consc_ideal_par3_t3 ~ i3*1
# Unique Variances
consc_ideal_par1_t2 ~~ consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ consc_ideal_par3_t2
consc_ideal_par1_t3 ~~ consc_ideal_par1_t3
consc_ideal_par2_t3 ~~ consc_ideal_par2_t3
consc_ideal_par3_t3 ~~ consc_ideal_par3_t3
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strong2_consc_ideal <- cfa(strong2_consc_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_consc_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_consc_ideal <- '
# Define the latent factors
consc_ideal1 =~ NA*consc_ideal_par1_t2 + lambda1*consc_ideal_par1_t2 + lambda2*consc_ideal_par2_t2 + lambda3*consc_ideal_par3_t2
consc_ideal2 =~ NA*consc_ideal_par1_t3 + lambda1*consc_ideal_par1_t3 + lambda2*consc_ideal_par2_t3 + lambda3*consc_ideal_par3_t3
# Intercepts
consc_ideal_par1_t2 ~ i1*1
consc_ideal_par2_t2 ~ i2*1
consc_ideal_par3_t2 ~ i3*1
consc_ideal_par1_t3 ~ i1*1
consc_ideal_par2_t3 ~ i2*1
consc_ideal_par3_t3 ~ i3*1
# Unique Variances
consc_ideal_par1_t2 ~~ u1*consc_ideal_par1_t2
consc_ideal_par2_t2 ~~ u2*consc_ideal_par2_t2
consc_ideal_par3_t2 ~~ u3*consc_ideal_par3_t2
consc_ideal_par1_t3 ~~ u1*consc_ideal_par1_t3
consc_ideal_par2_t3 ~~ u2*consc_ideal_par2_t3
consc_ideal_par3_t3 ~~ u3*consc_ideal_par3_t3
# Latent Variable Means
consc_ideal1 ~ 0*1
consc_ideal2 ~ 1
# Latent Variable Variances and Covariance
consc_ideal1 ~~ 1*consc_ideal1
consc_ideal2 ~~ consc_ideal2
consc_ideal1 ~~ consc_ideal2
'
fit_strict2_consc_ideal <- cfa(strict2_consc_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_consc_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_consc_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 70.8 4208. 4291. 0.959 0.923 0.117 0.0291
2 weak 577 17 73.4 4206. 4281. 0.959 0.938 0.105 0.0542
3 strong 577 15 75.8 4205. 4270. 0.958 0.948 0.0960 0.0561
4 strict 577 12 91.9 4215. 4267. 0.950 0.950 0.0942 0.0898# chi-square difference test for nested models
anova(fit_configural2_consc_ideal, fit_weak2_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_consc_ideal 8 4207.9 4290.7 70.821
fit_weak2_consc_ideal 10 4206.4 4280.5 73.376 2.5544 0.021919 2
Pr(>Chisq)
fit_configural2_consc_ideal
fit_weak2_consc_ideal 0.2788anova(fit_weak2_consc_ideal, fit_strong2_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_consc_ideal 10 4206.4 4280.5 73.376
fit_strong2_consc_ideal 12 4204.8 4270.2 75.801 2.4256 0.019204 2
Pr(>Chisq)
fit_weak2_consc_ideal
fit_strong2_consc_ideal 0.2974anova(fit_strong2_consc_ideal, fit_strict2_consc_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_consc_ideal 12 4204.8 4270.2 75.801
fit_strict2_consc_ideal 15 4214.9 4267.2 91.852 16.051 0.086831 3
Pr(>Chisq)
fit_strong2_consc_ideal
fit_strict2_consc_ideal 0.001107 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + neuro_curr_par2_t2 + neuro_curr_par3_t2
neuro_curr2 =~ NA*neuro_curr_par1_t3 + lambda1*neuro_curr_par1_t3 + neuro_curr_par2_t3 + neuro_curr_par3_t3
# Intercepts
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
neuro_curr_par1_t3 ~ i1*1
neuro_curr_par2_t3 ~ 1
neuro_curr_par3_t3 ~ 1
# Unique Variances
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
neuro_curr_par1_t3 ~~ neuro_curr_par1_t3
neuro_curr_par2_t3 ~~ neuro_curr_par2_t3
neuro_curr_par3_t3 ~~ neuro_curr_par3_t3
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_configural2_neuro_curr <- cfa(configural2_neuro_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_neuro_curr, fit.measures = TRUE)
# Weak invariance model
weak2_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
neuro_curr2 =~ NA*neuro_curr_par1_t3 + lambda1*neuro_curr_par1_t3 + lambda2*neuro_curr_par2_t3 + lambda3*neuro_curr_par3_t3
# Intercepts
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ 1
neuro_curr_par3_t2 ~ 1
neuro_curr_par1_t3 ~ i1*1
neuro_curr_par2_t3 ~ 1
neuro_curr_par3_t3 ~ 1
# Unique Variances
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
neuro_curr_par1_t3 ~~ neuro_curr_par1_t3
neuro_curr_par2_t3 ~~ neuro_curr_par2_t3
neuro_curr_par3_t3 ~~ neuro_curr_par3_t3
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_weak2_neuro_curr <- cfa(weak2_neuro_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_neuro_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
neuro_curr2 =~ NA*neuro_curr_par1_t3 + lambda1*neuro_curr_par1_t3 + lambda2*neuro_curr_par2_t3 + lambda3*neuro_curr_par3_t3
# Intercepts
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
neuro_curr_par1_t3 ~ i1*1
neuro_curr_par2_t3 ~ i2*1
neuro_curr_par3_t3 ~ i3*1
# Unique Variances
neuro_curr_par1_t2 ~~ neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ neuro_curr_par3_t2
neuro_curr_par1_t3 ~~ neuro_curr_par1_t3
neuro_curr_par2_t3 ~~ neuro_curr_par2_t3
neuro_curr_par3_t3 ~~ neuro_curr_par3_t3
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strong2_neuro_curr <- cfa(strong2_neuro_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_neuro_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_neuro_curr <- '
# Define the latent factors
neuro_curr1 =~ NA*neuro_curr_par1_t2 + lambda1*neuro_curr_par1_t2 + lambda2*neuro_curr_par2_t2 + lambda3*neuro_curr_par3_t2
neuro_curr2 =~ NA*neuro_curr_par1_t3 + lambda1*neuro_curr_par1_t3 + lambda2*neuro_curr_par2_t3 + lambda3*neuro_curr_par3_t3
# Intercepts
neuro_curr_par1_t2 ~ i1*1
neuro_curr_par2_t2 ~ i2*1
neuro_curr_par3_t2 ~ i3*1
neuro_curr_par1_t3 ~ i1*1
neuro_curr_par2_t3 ~ i2*1
neuro_curr_par3_t3 ~ i3*1
# Unique Variances
neuro_curr_par1_t2 ~~ u1*neuro_curr_par1_t2
neuro_curr_par2_t2 ~~ u2*neuro_curr_par2_t2
neuro_curr_par3_t2 ~~ u3*neuro_curr_par3_t2
neuro_curr_par1_t3 ~~ u1*neuro_curr_par1_t3
neuro_curr_par2_t3 ~~ u2*neuro_curr_par2_t3
neuro_curr_par3_t3 ~~ u3*neuro_curr_par3_t3
# Latent Variable Means
neuro_curr1 ~ 0*1
neuro_curr2 ~ 1
# Latent Variable Variances and Covariance
neuro_curr1 ~~ 1*neuro_curr1
neuro_curr2 ~~ neuro_curr2
neuro_curr1 ~~ neuro_curr2
'
fit_strict2_neuro_curr <- cfa(strict2_neuro_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_neuro_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_neuro_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 99.1 5872. 5955. 0.968 0.939 0.140 0.0219
2 weak 577 17 99.4 5868. 5942. 0.968 0.952 0.124 0.0232
3 strong 577 15 101. 5866. 5931. 0.968 0.961 0.113 0.0232
4 strict 577 12 101. 5860. 5913. 0.969 0.969 0.0999 0.0243# chi-square difference test for nested models
anova(fit_configural2_neuro_curr, fit_weak2_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_neuro_curr 8 5872.1 5954.9 99.065
fit_weak2_neuro_curr 10 5868.4 5942.5 99.397 0.33158 0 2
Pr(>Chisq)
fit_configural2_neuro_curr
fit_weak2_neuro_curr 0.8472anova(fit_weak2_neuro_curr, fit_strong2_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_neuro_curr 10 5868.4 5942.5 99.397
fit_strong2_neuro_curr 12 5865.6 5930.9 100.582 1.1852 0 2
Pr(>Chisq)
fit_weak2_neuro_curr
fit_strong2_neuro_curr 0.5529anova(fit_strong2_neuro_curr, fit_strict2_neuro_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_neuro_curr 12 5865.6 5930.9 100.58
fit_strict2_neuro_curr 15 5860.4 5912.7 101.46 0.87338 0 3
Pr(>Chisq)
fit_strong2_neuro_curr
fit_strict2_neuro_curr 0.8318Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + neuro_ideal_par2_t2 + neuro_ideal_par3_t2
neuro_ideal2 =~ NA*neuro_ideal_par1_t3 + lambda1*neuro_ideal_par1_t3 + neuro_ideal_par2_t3 + neuro_ideal_par3_t3
# Intercepts
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
neuro_ideal_par1_t3 ~ i1*1
neuro_ideal_par2_t3 ~ 1
neuro_ideal_par3_t3 ~ 1
# Unique Variances
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
neuro_ideal_par1_t3 ~~ neuro_ideal_par1_t3
neuro_ideal_par2_t3 ~~ neuro_ideal_par2_t3
neuro_ideal_par3_t3 ~~ neuro_ideal_par3_t3
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_configural2_neuro_ideal <- cfa(configural2_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_neuro_ideal, fit.measures = TRUE)
# Weak invariance model
weak2_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
neuro_ideal2 =~ NA*neuro_ideal_par1_t3 + lambda1*neuro_ideal_par1_t3 + lambda2*neuro_ideal_par2_t3 + lambda3*neuro_ideal_par3_t3
# Intercepts
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ 1
neuro_ideal_par3_t2 ~ 1
neuro_ideal_par1_t3 ~ i1*1
neuro_ideal_par2_t3 ~ 1
neuro_ideal_par3_t3 ~ 1
# Unique Variances
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
neuro_ideal_par1_t3 ~~ neuro_ideal_par1_t3
neuro_ideal_par2_t3 ~~ neuro_ideal_par2_t3
neuro_ideal_par3_t3 ~~ neuro_ideal_par3_t3
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_weak2_neuro_ideal <- cfa(weak2_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_neuro_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
neuro_ideal2 =~ NA*neuro_ideal_par1_t3 + lambda1*neuro_ideal_par1_t3 + lambda2*neuro_ideal_par2_t3 + lambda3*neuro_ideal_par3_t3
# Intercepts
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
neuro_ideal_par1_t3 ~ i1*1
neuro_ideal_par2_t3 ~ i2*1
neuro_ideal_par3_t3 ~ i3*1
# Unique Variances
neuro_ideal_par1_t2 ~~ neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ neuro_ideal_par3_t2
neuro_ideal_par1_t3 ~~ neuro_ideal_par1_t3
neuro_ideal_par2_t3 ~~ neuro_ideal_par2_t3
neuro_ideal_par3_t3 ~~ neuro_ideal_par3_t3
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strong2_neuro_ideal <- cfa(strong2_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_neuro_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_neuro_ideal <- '
# Define the latent factors
neuro_ideal1 =~ NA*neuro_ideal_par1_t2 + lambda1*neuro_ideal_par1_t2 + lambda2*neuro_ideal_par2_t2 + lambda3*neuro_ideal_par3_t2
neuro_ideal2 =~ NA*neuro_ideal_par1_t3 + lambda1*neuro_ideal_par1_t3 + lambda2*neuro_ideal_par2_t3 + lambda3*neuro_ideal_par3_t3
# Intercepts
neuro_ideal_par1_t2 ~ i1*1
neuro_ideal_par2_t2 ~ i2*1
neuro_ideal_par3_t2 ~ i3*1
neuro_ideal_par1_t3 ~ i1*1
neuro_ideal_par2_t3 ~ i2*1
neuro_ideal_par3_t3 ~ i3*1
# Unique Variances
neuro_ideal_par1_t2 ~~ u1*neuro_ideal_par1_t2
neuro_ideal_par2_t2 ~~ u2*neuro_ideal_par2_t2
neuro_ideal_par3_t2 ~~ u3*neuro_ideal_par3_t2
neuro_ideal_par1_t3 ~~ u1*neuro_ideal_par1_t3
neuro_ideal_par2_t3 ~~ u2*neuro_ideal_par2_t3
neuro_ideal_par3_t3 ~~ u3*neuro_ideal_par3_t3
# Latent Variable Means
neuro_ideal1 ~ 0*1
neuro_ideal2 ~ 1
# Latent Variable Variances and Covariance
neuro_ideal1 ~~ 1*neuro_ideal1
neuro_ideal2 ~~ neuro_ideal2
neuro_ideal1 ~~ neuro_ideal2
'
fit_strict2_neuro_ideal <- cfa(strict2_neuro_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_neuro_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_neuro_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 37.4 4514. 4596. 0.978 0.958 0.0798 0.0219
2 weak 577 17 40.1 4512. 4586. 0.977 0.966 0.0722 0.0309
3 strong 577 15 43.1 4511. 4577. 0.976 0.971 0.0670 0.0364
4 strict 577 12 48.3 4510. 4563. 0.975 0.975 0.0620 0.0280# chi-square difference test for nested models
anova(fit_configural2_neuro_ideal, fit_weak2_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_neuro_ideal 8 4513.5 4596.3 37.415
fit_weak2_neuro_ideal 10 4512.2 4586.3 40.113 2.6987 0.024606 2
Pr(>Chisq)
fit_configural2_neuro_ideal
fit_weak2_neuro_ideal 0.2594anova(fit_weak2_neuro_ideal, fit_strong2_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_neuro_ideal 10 4512.2 4586.3 40.113
fit_strong2_neuro_ideal 12 4511.3 4576.6 43.122 3.0084 0.02956 2
Pr(>Chisq)
fit_weak2_neuro_ideal
fit_strong2_neuro_ideal 0.2222anova(fit_strong2_neuro_ideal, fit_strict2_neuro_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_neuro_ideal 12 4511.3 4576.6 43.122
fit_strict2_neuro_ideal 15 4510.4 4562.7 48.255 5.1328 0.035101 3
Pr(>Chisq)
fit_strong2_neuro_ideal
fit_strict2_neuro_ideal 0.1623Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + openn_curr_par2_t2 + openn_curr_par3_t2
openn_curr2 =~ NA*openn_curr_par1_t3 + lambda1*openn_curr_par1_t3 + openn_curr_par2_t3 + openn_curr_par3_t3
# Intercepts
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
openn_curr_par1_t3 ~ i1*1
openn_curr_par2_t3 ~ 1
openn_curr_par3_t3 ~ 1
# Unique Variances
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
openn_curr_par1_t3 ~~ openn_curr_par1_t3
openn_curr_par2_t3 ~~ openn_curr_par2_t3
openn_curr_par3_t3 ~~ openn_curr_par3_t3
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_configural2_openn_curr <- cfa(configural2_openn_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_openn_curr, fit.measures = TRUE)
# Weak invariance model
weak2_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
openn_curr2 =~ NA*openn_curr_par1_t3 + lambda1*openn_curr_par1_t3 + lambda2*openn_curr_par2_t3 + lambda3*openn_curr_par3_t3
# Intercepts
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ 1
openn_curr_par3_t2 ~ 1
openn_curr_par1_t3 ~ i1*1
openn_curr_par2_t3 ~ 1
openn_curr_par3_t3 ~ 1
# Unique Variances
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
openn_curr_par1_t3 ~~ openn_curr_par1_t3
openn_curr_par2_t3 ~~ openn_curr_par2_t3
openn_curr_par3_t3 ~~ openn_curr_par3_t3
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_weak2_openn_curr <- cfa(weak2_openn_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_openn_curr, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
openn_curr2 =~ NA*openn_curr_par1_t3 + lambda1*openn_curr_par1_t3 + lambda2*openn_curr_par2_t3 + lambda3*openn_curr_par3_t3
# Intercepts
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
openn_curr_par1_t3 ~ i1*1
openn_curr_par2_t3 ~ i2*1
openn_curr_par3_t3 ~ i3*1
# Unique Variances
openn_curr_par1_t2 ~~ openn_curr_par1_t2
openn_curr_par2_t2 ~~ openn_curr_par2_t2
openn_curr_par3_t2 ~~ openn_curr_par3_t2
openn_curr_par1_t3 ~~ openn_curr_par1_t3
openn_curr_par2_t3 ~~ openn_curr_par2_t3
openn_curr_par3_t3 ~~ openn_curr_par3_t3
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strong2_openn_curr <- cfa(strong2_openn_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_openn_curr, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_openn_curr <- '
# Define the latent factors
openn_curr1 =~ NA*openn_curr_par1_t2 + lambda1*openn_curr_par1_t2 + lambda2*openn_curr_par2_t2 + lambda3*openn_curr_par3_t2
openn_curr2 =~ NA*openn_curr_par1_t3 + lambda1*openn_curr_par1_t3 + lambda2*openn_curr_par2_t3 + lambda3*openn_curr_par3_t3
# Intercepts
openn_curr_par1_t2 ~ i1*1
openn_curr_par2_t2 ~ i2*1
openn_curr_par3_t2 ~ i3*1
openn_curr_par1_t3 ~ i1*1
openn_curr_par2_t3 ~ i2*1
openn_curr_par3_t3 ~ i3*1
# Unique Variances
openn_curr_par1_t2 ~~ u1*openn_curr_par1_t2
openn_curr_par2_t2 ~~ u2*openn_curr_par2_t2
openn_curr_par3_t2 ~~ u3*openn_curr_par3_t2
openn_curr_par1_t3 ~~ u1*openn_curr_par1_t3
openn_curr_par2_t3 ~~ u2*openn_curr_par2_t3
openn_curr_par3_t3 ~~ u3*openn_curr_par3_t3
# Latent Variable Means
openn_curr1 ~ 0*1
openn_curr2 ~ 1
# Latent Variable Variances and Covariance
openn_curr1 ~~ 1*openn_curr1
openn_curr2 ~~ openn_curr2
openn_curr1 ~~ openn_curr2
'
fit_strict2_openn_curr <- cfa(strict2_openn_curr, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_openn_curr, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_openn_curr) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 577 19 208. 5257. 5340. 0.911 0.834 0.208 0.0423
2 weak 577 17 209. 5254. 5328. 0.912 0.868 0.186 0.0439
3 strong 577 15 209. 5250. 5315. 0.913 0.891 0.169 0.0437
4 strict 577 12 212. 5247. 5299. 0.913 0.913 0.151 0.0447# chi-square difference test for nested models
anova(fit_configural2_openn_curr, fit_weak2_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_openn_curr 8 5257.1 5339.9 207.96
fit_weak2_openn_curr 10 5253.9 5328.0 208.72 0.76522 0 2
Pr(>Chisq)
fit_configural2_openn_curr
fit_weak2_openn_curr 0.6821anova(fit_weak2_openn_curr, fit_strong2_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_openn_curr 10 5253.9 5328.0 208.72
fit_strong2_openn_curr 12 5249.9 5315.3 208.75 0.028496 0 2
Pr(>Chisq)
fit_weak2_openn_curr
fit_strong2_openn_curr 0.9859anova(fit_strong2_openn_curr, fit_strict2_openn_curr)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_openn_curr 12 5249.9 5315.3 208.75
fit_strict2_openn_curr 15 5246.9 5299.2 211.71 2.9624 0 3
Pr(>Chisq)
fit_strong2_openn_curr
fit_strict2_openn_curr 0.3975Fit model:
# Code snippets adapted from https://quantdev.ssri.psu.edu/sites/qdev/files/LongitudinalMeasurementInvariance_2017_1108.html
# Configural invariance model
configural2_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + openn_ideal_par2_t2 + openn_ideal_par3_t2
openn_ideal2 =~ NA*openn_ideal_par1_t3 + lambda1*openn_ideal_par1_t3 + openn_ideal_par2_t3 + openn_ideal_par3_t3
# Intercepts
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
openn_ideal_par1_t3 ~ i1*1
openn_ideal_par2_t3 ~ 1
openn_ideal_par3_t3 ~ 1
# Unique Variances
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
openn_ideal_par1_t3 ~~ openn_ideal_par1_t3
openn_ideal_par2_t3 ~~ openn_ideal_par2_t3
openn_ideal_par3_t3 ~~ openn_ideal_par3_t3
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_configural2_openn_ideal <- cfa(configural2_openn_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_configural2_openn_ideal, fit.measures = TRUE)
# Weak invariance model
weak2_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
openn_ideal2 =~ NA*openn_ideal_par1_t3 + lambda1*openn_ideal_par1_t3 + lambda2*openn_ideal_par2_t3 + lambda3*openn_ideal_par3_t3
# Intercepts
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ 1
openn_ideal_par3_t2 ~ 1
openn_ideal_par1_t3 ~ i1*1
openn_ideal_par2_t3 ~ 1
openn_ideal_par3_t3 ~ 1
# Unique Variances
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
openn_ideal_par1_t3 ~~ openn_ideal_par1_t3
openn_ideal_par2_t3 ~~ openn_ideal_par2_t3
openn_ideal_par3_t3 ~~ openn_ideal_par3_t3
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_weak2_openn_ideal <- cfa(weak2_openn_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_weak2_openn_ideal, fit.measures = TRUE)
# Strong invariance model (additional constraints on manifest intercepts)
strong2_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
openn_ideal2 =~ NA*openn_ideal_par1_t3 + lambda1*openn_ideal_par1_t3 + lambda2*openn_ideal_par2_t3 + lambda3*openn_ideal_par3_t3
# Intercepts
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
openn_ideal_par1_t3 ~ i1*1
openn_ideal_par2_t3 ~ i2*1
openn_ideal_par3_t3 ~ i3*1
# Unique Variances
openn_ideal_par1_t2 ~~ openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ openn_ideal_par3_t2
openn_ideal_par1_t3 ~~ openn_ideal_par1_t3
openn_ideal_par2_t3 ~~ openn_ideal_par2_t3
openn_ideal_par3_t3 ~~ openn_ideal_par3_t3
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strong2_openn_ideal <- cfa(strong2_openn_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strong2_openn_ideal, fit.measures = TRUE)
# Strict invariance model (additional constraints on manifest variances)
strict2_openn_ideal <- '
# Define the latent factors
openn_ideal1 =~ NA*openn_ideal_par1_t2 + lambda1*openn_ideal_par1_t2 + lambda2*openn_ideal_par2_t2 + lambda3*openn_ideal_par3_t2
openn_ideal2 =~ NA*openn_ideal_par1_t3 + lambda1*openn_ideal_par1_t3 + lambda2*openn_ideal_par2_t3 + lambda3*openn_ideal_par3_t3
# Intercepts
openn_ideal_par1_t2 ~ i1*1
openn_ideal_par2_t2 ~ i2*1
openn_ideal_par3_t2 ~ i3*1
openn_ideal_par1_t3 ~ i1*1
openn_ideal_par2_t3 ~ i2*1
openn_ideal_par3_t3 ~ i3*1
# Unique Variances
openn_ideal_par1_t2 ~~ u1*openn_ideal_par1_t2
openn_ideal_par2_t2 ~~ u2*openn_ideal_par2_t2
openn_ideal_par3_t2 ~~ u3*openn_ideal_par3_t2
openn_ideal_par1_t3 ~~ u1*openn_ideal_par1_t3
openn_ideal_par2_t3 ~~ u2*openn_ideal_par2_t3
openn_ideal_par3_t3 ~~ u3*openn_ideal_par3_t3
# Latent Variable Means
openn_ideal1 ~ 0*1
openn_ideal2 ~ 1
# Latent Variable Variances and Covariance
openn_ideal1 ~~ 1*openn_ideal1
openn_ideal2 ~~ openn_ideal2
openn_ideal1 ~~ openn_ideal2
'
fit_strict2_openn_ideal <- cfa(strict2_openn_ideal, data = df_sbsa2_wide_pers %>% filter(valid_t2==1 & rando!="Waitlist-Control"), mimic = "mplus", missing="ML")
summary(fit_strict2_openn_ideal, fit.measures = TRUE)Results summary:
# compare model fit
bind_rows(broom::glance(fit_configural2_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_weak2_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strong2_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr),
broom::glance(fit_strict2_openn_ideal) %>%
select(nobs, npar, chisq, AIC, BIC, cfi, tli, rmsea, srmr)) %>%
mutate(model = c("configural", "weak", "strong", "strict")) %>%
select(model, everything())# A tibble: 4 × 10
model nobs npar chisq AIC BIC cfi tli rmsea srmr
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 configural 576 19 116. 5248. 5331. 0.905 0.822 0.153 0.0658
2 weak 576 17 117. 5245. 5319. 0.906 0.859 0.136 0.0712
3 strong 576 15 121. 5246. 5311. 0.904 0.880 0.126 0.0739
4 strict 576 12 123. 5241. 5294. 0.905 0.905 0.112 0.0683# chi-square difference test for nested models
anova(fit_configural2_openn_ideal, fit_weak2_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_configural2_openn_ideal 8 5248.2 5330.9 115.88
fit_weak2_openn_ideal 10 5245.4 5319.4 117.09 1.2118 0 2
Pr(>Chisq)
fit_configural2_openn_ideal
fit_weak2_openn_ideal 0.5456anova(fit_weak2_openn_ideal, fit_strong2_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_weak2_openn_ideal 10 5245.4 5319.4 117.09
fit_strong2_openn_ideal 12 5245.7 5311.0 121.41 4.3138 0.044817 2
Pr(>Chisq)
fit_weak2_openn_ideal
fit_strong2_openn_ideal 0.1157anova(fit_strong2_openn_ideal, fit_strict2_openn_ideal)
Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff RMSEA Df diff
fit_strong2_openn_ideal 12 5245.7 5311.0 121.41
fit_strict2_openn_ideal 15 5241.5 5293.8 123.19 1.7786 0 3
Pr(>Chisq)
fit_strong2_openn_ideal
fit_strict2_openn_ideal 0.6196How many participants at each time point and in each condition?
df_sbsa2 %>% group_by(time, rando) %>% tally()# A tibble: 9 × 3
# Groups: time [3]
time rando n
<dbl> <chr> <int>
1 1 Self-Acceptance 336
2 1 Self-Improvement 338
3 1 Waitlist-Control 174
4 2 Self-Acceptance 284
5 2 Self-Improvement 293
6 2 Waitlist-Control 147
7 3 Self-Acceptance 252
8 3 Self-Improvement 262
9 3 Waitlist-Control 125Gender and age distribution
df_sbsa2 %>% group_by(time, rando) %>%
summarise(n = n(), m_age = mean(age, na.rm=T), sd_age = sd(age, na.rm=T), min_age = min(age, na.rm=T), max_age = max(age, na.rm=T))# A tibble: 9 × 7
# Groups: time [3]
time rando n m_age sd_age min_age max_age
<dbl> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 1 Self-Acceptance 336 30.5 8.87 20 65
2 1 Self-Improvement 338 30.3 9.38 18 69
3 1 Waitlist-Control 174 31.2 11.2 19 75
4 2 Self-Acceptance 284 30.8 8.98 20 65
5 2 Self-Improvement 293 30.3 9.30 20 69
6 2 Waitlist-Control 147 31.9 11.8 19 75
7 3 Self-Acceptance 252 31.1 8.96 20 65
8 3 Self-Improvement 262 30.7 9.41 20 69
9 3 Waitlist-Control 125 32.4 12.3 19 75df_sbsa2 %>% group_by(time, rando, gender) %>%
summarise(n = n(), m_age = mean(age, na.rm=T), sd_age = sd(age, na.rm=T), min_age = min(age, na.rm=T), max_age = max(age, na.rm=T))# A tibble: 18 × 8
# Groups: time, rando [9]
time rando gender n m_age sd_age min_age max_age
<dbl> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
1 1 Self-Acceptance Female 169 31 9.82 20 65
2 1 Self-Acceptance Male 167 30.0 7.79 20 58
3 1 Self-Improvement Female 166 31.0 9.15 20 65
4 1 Self-Improvement Male 172 29.8 9.58 18 69
5 1 Waitlist-Control Female 85 31.9 11.4 21 69
6 1 Waitlist-Control Male 89 30.6 11.0 19 75
7 2 Self-Acceptance Female 142 30.9 9.83 20 65
8 2 Self-Acceptance Male 142 30.8 8.07 20 58
9 2 Self-Improvement Female 143 30.8 8.82 20 59
10 2 Self-Improvement Male 150 29.9 9.75 20 69
11 2 Waitlist-Control Female 75 33.0 12.4 21 69
12 2 Waitlist-Control Male 72 30.7 11.1 19 75
13 3 Self-Acceptance Female 127 30.8 9.64 20 65
14 3 Self-Acceptance Male 125 31.5 8.23 20 58
15 3 Self-Improvement Female 126 31 8.65 20 56
16 3 Self-Improvement Male 136 30.4 10.1 20 69
17 3 Waitlist-Control Female 65 33.6 12.9 21 69
18 3 Waitlist-Control Male 60 31.0 11.6 19 75Summary stats:
table(df_sbsa2$sb06_01) # How much do you want to change your personality in general?
1 2 3 4 5
34 81 179 89 27 summary(df_sbsa2$sb06_01) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 2.000 3.000 2.985 4.000 5.000 1801 table(df_sbsa2$sa06_01) # How much do you want to be better at accepting yourself for who you are?
1 2 3 4 5
9 12 65 121 203 summary(df_sbsa2$sa06_01) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.000 4.000 4.000 4.212 5.000 5.000 1801 Plot:
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb06_01)), aes(x=sb06_01)) + geom_histogram(bins=5) +
labs(x="General change goal", y="Frequency") + theme_bw(),
ggplot(df_sbsa2 %>% filter(!is.na(sa06_01)), aes(x=sa06_01)) + geom_histogram(bins=5) +
labs(x="General acceptance goal", y="Frequency") + theme_bw(),
ncol = 2
)
Self-Improvement
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you [want to] change. Please rate how much you would like to change your level of each of these traits, where 1 = I do not want to change and 5 = I want to change a lot.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to change in some of these traits. We want to know how you think you changed in each trait. Please rate how much you changed your level of each of these traits during this study, where 1 = I am completely the same and 5 = I have changed a lot.
Self Acceptance
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you accept yourself. Please rate how much you accept your level of each of these traits, where 1 = I completely accept myself and 5 = I want to accept myself more.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to accept your level on some of these traits more. We want to know how much you think you accept yourself more because of this study. Please rate how much you accept your level of each of these traits more during this study, where 1 = I accept myself the same and 5 = I accept this about myself much more.
Plot distributions
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 01], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_01)), aes(x=sb07_01)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_01)), aes(x=sb12_01)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_01)), aes(x=sa07_01)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_01)), aes(x=sa14_01)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 02], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_02)), aes(x=sb07_02)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_02)), aes(x=sb12_02)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_02)), aes(x=sa07_02)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_02)), aes(x=sa14_02)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 03], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_03)), aes(x=sb07_03)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_03)), aes(x=sb12_03)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_03)), aes(x=sa07_03)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_03)), aes(x=sa14_03)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 04], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_04)), aes(x=sb07_04)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_04)), aes(x=sb12_04)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_04)), aes(x=sa07_04)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_04)), aes(x=sa14_04)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 05], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_05)), aes(x=sb07_05)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175, 200), limits = c(0,220)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_05)), aes(x=sb12_05)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175, 200), limits = c(0,220)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_05)), aes(x=sa07_05)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175, 200), limits = c(0,220)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_05)), aes(x=sa14_05)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175, 200), limits = c(0,220)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 06], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_06)), aes(x=sb07_06)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_06)), aes(x=sb12_06)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_06)), aes(x=sa07_06)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_06)), aes(x=sa14_06)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 07], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_07)), aes(x=sb07_07)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_07)), aes(x=sb12_07)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_07)), aes(x=sa07_07)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_07)), aes(x=sa14_07)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 08], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_08)), aes(x=sb07_08)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_08)), aes(x=sb12_08)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_08)), aes(x=sa07_08)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_08)), aes(x=sa14_08)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 09], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_09)), aes(x=sb07_09)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_09)), aes(x=sb12_09)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_09)), aes(x=sa07_09)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_09)), aes(x=sa14_09)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 10], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_10)), aes(x=sb07_10)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175), limits = c(0,200)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_10)), aes(x=sb12_10)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175), limits = c(0,200)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_10)), aes(x=sa07_10)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175), limits = c(0,200)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_10)), aes(x=sa14_10)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150, 175), limits = c(0,200)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 11], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_11)), aes(x=sb07_11)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_11)), aes(x=sb12_11)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_11)), aes(x=sa07_11)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_11)), aes(x=sa14_11)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 12], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_12)), aes(x=sb07_12)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_12)), aes(x=sb12_12)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_12)), aes(x=sa07_12)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_12)), aes(x=sa14_12)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
cowplot::plot_grid(
ggdraw() + draw_label(names(b5_vars)[5 + 13], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_13)), aes(x=sb07_13)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_13)), aes(x=sb12_13)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_13)), aes(x=sa07_13)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_13)), aes(x=sa14_13)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 14], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_14)), aes(x=sb07_14)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_14)), aes(x=sb12_14)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_14)), aes(x=sa07_14)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_14)), aes(x=sa14_14)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ggdraw() + draw_label(names(b5_vars)[5 + 15], fontface = 'plain', x = 0, hjust = 0) + theme(plot.margin = margin(0, 0, 0, 7)),
cowplot::plot_grid(
ggplot(df_sbsa2 %>% filter(!is.na(sb07_15)), aes(x=sb07_15)) + geom_histogram(bins=5) +
labs(x="Change goal: Pre", y="Frequency") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sb12_15)), aes(x=sb12_15)) + geom_histogram(bins=5) +
labs(x="Change goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa07_15)), aes(x=sa07_15)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Pre", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ggplot(df_sbsa2 %>% filter(!is.na(sa14_15)), aes(x=sa14_15)) + geom_histogram(bins=5) +
labs(x="Acceptance goal: Post", y="") + theme_bw() + scale_y_continuous(breaks = c(25, 50, 75, 100, 125, 150), limits = c(0,170)),
ncol = 4),
ncol = 1,
rel_heights = c(0.1, 1, 0.1, 1, 0.1, 1)
)
All four psychological well-being indicators will be positively correlated with a greater similarity between current- and ideal self-ratings of personality.
To examine this at the level of overall profiles, we will compute the correlations between the psychological well-being indicators and the Fisher z transformed correlations between the facet- and item-level real-ideal self-profiles. To examine this at the level of individual traits, we will compute the correlation between psychological well-being indicators and the squared difference between current- and ideal-self rating for each Big Five trait and facet.
Computations:
cormat_profile <- cor(df_sbsa2[, c("swls", "meaning", "selfes", "concept",
"profile_corr_item_z", "profile_corr_facet_z")], use = "pairwise.complete.obs")
rownames(cormat_profile) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
"Item-Level Profile Corr (z)", "Facet-Level Profile Corr (z)")
colnames(cormat_profile) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
"Item-Level Profile Corr (z)", "Facet-Level Profile Corr (z)")corrplot(cormat_profile, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.7, diag = FALSE) # also add numbers
Computations:
cormat_sqtraits <- cor(df_sbsa2[, c("swls", "meaning", "selfes", "concept",
paste0(str_trunc(names(b5_vars)[1:5], 5, ellipsis = ""), "_sqdiff"))],
use = "pairwise.complete.obs")
rownames(cormat_sqtraits) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[1:5]))
colnames(cormat_sqtraits) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[1:5]))
cormat_sqfacets <- cor(df_sbsa2[, c("swls", "meaning", "selfes", "concept",
paste0(str_trunc(names(b5_vars)[6:20], 5, ellipsis = ""), "_sqdiff"))],
use = "pairwise.complete.obs")
rownames(cormat_sqfacets) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[6:20]))
colnames(cormat_sqfacets) <- c("Life Satisfaction", "Meaning in Life", "Self-Esteem" , "Self Concept Clarity",
str_to_title(names(b5_vars)[6:20]))Big Five traits
corrplot(cormat_sqtraits, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.7, diag = FALSE) # also add numbers
Big Five facets
corrplot(cormat_sqfacets, type = "lower", order = "original", tl.col = "black", tl.srt = 10,
addCoef.col ='black', number.cex = 0.6, diag = FALSE) # also add numbers
Both intervention groups (but not the control group between T1 and T2) will increase in all four psychological well-being indicators.
We will test the mean-level difference between baseline and follow up using latent change models. Our main focus will be changes from T1 to T2 and we will check in a second set of models whether any changes persist from T2 to T3.
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_swls_hyp2 <- '
swls_t1 =~ 1*sw06_01_t1 + lamb2*sw06_02_t1 + lamb3*sw06_03_t1 + lamb4*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + lamb2*sw06_02_t2 + lamb3*sw06_03_t2 + lamb4*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ 1 # This estimates the intercept of the change score
swls_t1 ~ 1 # This estimates the intercept of swls_t1
d_swls_1 ~~ d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ res1*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ res2*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ res3*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ res4*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ res1*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ res2*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ res3*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ res4*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ m4*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ m4*1 # This estimates the intercept of X4 at T2
'
fit_mi_lcs_swls_hyp2 <- lavaan(mi_lcs_swls_hyp2, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_swls_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_swls_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 673 | 19 | 13061.93 | 13147.66 | 0.977 | 0.065 | 0.03 |
# parameters of interest
params_lcs_swls_hyp2 <- broom::tidy(fit_mi_lcs_swls_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("swls_t2 ~ swls_t1", "d_swls_1 =~ swls_t2", "d_swls_1 ~ swls_t1", # change parameters
"d_swls_1 ~1 ", "swls_t1 ~1 ", "", # means
"d_swls_1 ~~ d_swls_1")) # variances
kable(params_lcs_swls_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| swls_t2 ~ swls_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_swls_1 =~ swls_t2 | 1.000 | 1.000 | 1.000 | 0.653 | NA | NA |
| d_swls_1 ~1 | 0.818 | 0.618 | 1.018 | 1.246 | 8.014 | 0 |
| swls_t1 ~1 | 2.685 | 2.598 | 2.772 | 2.716 | 60.416 | 0 |
| d_swls_1 ~~ d_swls_1 | 0.390 | 0.307 | 0.473 | 0.906 | 9.220 | 0 |
| d_swls_1 ~ swls_t1 | -0.204 | -0.270 | -0.137 | -0.307 | -5.982 | 0 |
Plot model:
semPaths(fit_mi_lcs_swls_hyp2, what="est",
sizeLat = 7, sizeMan = 7, edge.label.cex = .75, intercepts = T, edge.color="black")
What happens in the waitlist control group during this time?
Fit model:
fit_mi_lcs_swls_hyp2_cg <- lavaan(mi_lcs_swls_hyp2, data=df_sbsa2_wide_wb %>% filter(rando=="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_swls_hyp2_cg, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_swls_hyp2_cg) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 175 | 19 | 3244.162 | 3304.293 | 0.978 | 0.071 | 0.037 |
# parameters of interest
params_lcs_swls_hyp2_cg <- broom::tidy(fit_mi_lcs_swls_hyp2_cg, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("swls_t2 ~ swls_t1", "d_swls_1 =~ swls_t2", "d_swls_1 ~ swls_t1", # change parameters
"d_swls_1 ~1 ", "swls_t1 ~1 ", "", # means
"d_swls_1 ~~ d_swls_1")) # variances
kable(params_lcs_swls_hyp2_cg, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| swls_t2 ~ swls_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_swls_1 =~ swls_t2 | 1.000 | 1.000 | 1.000 | 0.481 | NA | NA |
| d_swls_1 ~1 | 0.510 | 0.172 | 0.848 | 0.951 | 2.955 | 0.003 |
| swls_t1 ~1 | 2.634 | 2.455 | 2.812 | 2.412 | 28.873 | 0.000 |
| d_swls_1 ~~ d_swls_1 | 0.276 | 0.132 | 0.420 | 0.960 | 3.751 | 0.000 |
| d_swls_1 ~ swls_t1 | -0.098 | -0.206 | 0.009 | -0.200 | -1.789 | 0.074 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_meaning_hyp2 <- '
meaning_t1 =~ 1*meaning_par1_t1 + lamb2*meaning_par2_t1 + lamb3*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + lamb2*meaning_par2_t2 + lamb3*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ 1 # This estimates the intercept of the change score
meaning_t1 ~ 1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ res1*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ res2*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ res3*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ res1*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ res2*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ res3*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_meaning_hyp2 <- lavaan(mi_lcs_meaning_hyp2, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_meaning_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_meaning_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 673 | 15 | 9545.085 | 9612.761 | 0.992 | 0.055 | 0.025 |
# parameters of interest
params_lcs_meaning_hyp2 <- broom::tidy(fit_mi_lcs_meaning_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("meaning_t2 ~ meaning_t1", "d_meaning_1 =~ meaning_t2", "d_meaning_1 ~ meaning_t1", # change parameters
"d_meaning_1 ~1 ", "meaning_t1 ~1 ", "", # means
"d_meaning_1 ~~ d_meaning_1")) # variances
kable(params_lcs_meaning_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| meaning_t2 ~ meaning_t1 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_meaning_1 =~ meaning_t2 | 1.000 | 1.000 | 1.000 | 0.698 | NA | NA |
| d_meaning_1 ~1 | 1.021 | 0.796 | 1.246 | 1.527 | 8.899 | 0 |
| meaning_t1 ~1 | 3.209 | 3.123 | 3.294 | 3.292 | 73.486 | 0 |
| d_meaning_1 ~~ d_meaning_1 | 0.387 | 0.297 | 0.476 | 0.865 | 8.477 | 0 |
| d_meaning_1 ~ meaning_t1 | -0.252 | -0.316 | -0.189 | -0.368 | -7.772 | 0 |
What happens in the waitlist control group during this time?
Fit model:
fit_mi_lcs_meaning_hyp2_cg <- lavaan(mi_lcs_meaning_hyp2, data=df_sbsa2_wide_wb %>% filter(rando=="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_meaning_hyp2_cg, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_meaning_hyp2_cg) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 175 | 15 | 2481.44 | 2528.912 | 1 | 0 | 0.033 |
# parameters of interest
params_lcs_meaning_hyp2_cg <- broom::tidy(fit_mi_lcs_meaning_hyp2_cg, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("meaning_t2 ~ meaning_t1", "d_meaning_1 =~ meaning_t2", "d_meaning_1 ~ meaning_t1", # change parameters
"d_meaning_1 ~1 ", "meaning_t1 ~1 ", "", # means
"d_meaning_1 ~~ d_meaning_1")) # variances
kable(params_lcs_meaning_hyp2_cg, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| meaning_t2 ~ meaning_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_meaning_1 =~ meaning_t2 | 1.000 | 1.000 | 1.000 | 0.662 | NA | NA |
| d_meaning_1 ~1 | 0.972 | 0.528 | 1.416 | 1.378 | 4.292 | 0.000 |
| meaning_t1 ~1 | 3.190 | 3.008 | 3.373 | 3.021 | 34.294 | 0.000 |
| d_meaning_1 ~~ d_meaning_1 | 0.447 | 0.295 | 0.599 | 0.898 | 5.770 | 0.000 |
| d_meaning_1 ~ meaning_t1 | -0.213 | -0.334 | -0.092 | -0.319 | -3.456 | 0.001 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_selfes_hyp2 <- '
selfes_t1 =~ 1*selfes_par1_t1 + lamb2*selfes_par2_t1 + lamb3*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + lamb2*selfes_par2_t2 + lamb3*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ 1 # This estimates the intercept of the change score
selfes_t1 ~ 1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ res1*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ res2*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ res3*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ res1*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ res2*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ res3*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_selfes_hyp2 <- lavaan(mi_lcs_selfes_hyp2, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_selfes_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_selfes_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 673 | 15 | 7206.632 | 7274.308 | 1 | 0 | 0.016 |
# parameters of interest
params_lcs_selfes_hyp2 <- broom::tidy(fit_mi_lcs_selfes_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("selfes_t2 ~ selfes_t1", "d_selfes_1 =~ selfes_t2", "d_selfes_1 ~ selfes_t1", # change parameters
"d_selfes_1 ~1 ", "selfes_t1 ~1 ", "", # means
"d_selfes_1 ~~ d_selfes_1")) # variances
kable(params_lcs_selfes_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| selfes_t2 ~ selfes_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_selfes_1 =~ selfes_t2 | 1.000 | 1.000 | 1.000 | 0.670 | NA | NA |
| d_selfes_1 ~1 | 1.126 | 0.901 | 1.352 | 2.335 | 9.779 | 0 |
| selfes_t1 ~1 | 3.540 | 3.476 | 3.604 | 4.745 | 108.567 | 0 |
| d_selfes_1 ~~ d_selfes_1 | 0.200 | 0.159 | 0.240 | 0.858 | 9.585 | 0 |
| d_selfes_1 ~ selfes_t1 | -0.244 | -0.303 | -0.185 | -0.377 | -8.119 | 0 |
What happens in the waitlist control group during this time?
Fit model:
fit_mi_lcs_selfes_hyp2_cg <- lavaan(mi_lcs_selfes_hyp2, data=df_sbsa2_wide_wb %>% filter(rando=="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_selfes_hyp2_cg, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_selfes_hyp2_cg) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 175 | 15 | 1724.661 | 1772.133 | 1 | 0 | 0.02 |
# parameters of interest
params_lcs_selfes_hyp2_cg <- broom::tidy(fit_mi_lcs_selfes_hyp2_cg, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("selfes_t2 ~ selfes_t1", "d_selfes_1 =~ selfes_t2", "d_selfes_1 ~ selfes_t1", # change parameters
"d_selfes_1 ~1 ", "selfes_t1 ~1 ", "", # means
"d_selfes_1 ~~ d_selfes_1")) # variances
kable(params_lcs_selfes_hyp2_cg, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| selfes_t2 ~ selfes_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_selfes_1 =~ selfes_t2 | 1.000 | 1.000 | 1.000 | 0.395 | NA | NA |
| d_selfes_1 ~1 | 0.429 | 0.163 | 0.695 | 1.346 | 3.163 | 0.002 |
| selfes_t1 ~1 | 3.545 | 3.414 | 3.675 | 4.443 | 53.386 | 0.000 |
| d_selfes_1 ~~ d_selfes_1 | 0.099 | 0.056 | 0.141 | 0.971 | 4.557 | 0.000 |
| d_selfes_1 ~ selfes_t1 | -0.068 | -0.139 | 0.002 | -0.171 | -1.891 | 0.059 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_concept_hyp2 <- '
concept_t1 =~ 1*concept_par1_t1 + lamb2*concept_par2_t1 + lamb3*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + lamb2*concept_par2_t2 + lamb3*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ 1 # This estimates the intercept of the change score
concept_t1 ~ 1 # This estimates the intercept of concept_t1
d_concept_1 ~~ d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ res1*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ res2*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ res3*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ res1*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ res2*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ res3*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_concept_hyp2 <- lavaan(mi_lcs_concept_hyp2, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_concept_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_concept_hyp2) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 673 | 15 | 7401.241 | 7468.917 | 0.996 | 0.036 | 0.019 |
# parameters of interest
params_lcs_concept_hyp2 <- broom::tidy(fit_mi_lcs_concept_hyp2, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("concept_t2 ~ concept_t1", "d_concept_1 =~ concept_t2", "d_concept_1 ~ concept_t1", # change parameters
"d_concept_1 ~1 ", "concept_t1 ~1 ", "", # means
"d_concept_1 ~~ d_concept_1")) # variances
kable(params_lcs_concept_hyp2, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| concept_t2 ~ concept_t1 | 1.000 | 1.000 | 1.000 | 1.021 | NA | NA |
| d_concept_1 =~ concept_t2 | 1.000 | 1.000 | 1.000 | 0.754 | NA | NA |
| d_concept_1 ~1 | 1.071 | 0.866 | 1.275 | 2.144 | 10.240 | 0 |
| concept_t1 ~1 | 3.044 | 2.983 | 3.104 | 4.502 | 98.796 | 0 |
| d_concept_1 ~~ d_concept_1 | 0.210 | 0.169 | 0.251 | 0.842 | 10.041 | 0 |
| d_concept_1 ~ concept_t1 | -0.294 | -0.357 | -0.230 | -0.397 | -9.022 | 0 |
What happens in the waitlist control group during this time?
Fit model:
fit_mi_lcs_concept_hyp2_cg <- lavaan(mi_lcs_concept_hyp2, data=df_sbsa2_wide_wb %>% filter(rando=="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_concept_hyp2_cg, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_concept_hyp2_cg) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 175 | 15 | 1805.904 | 1853.376 | 0.991 | 0.065 | 0.026 |
# parameters of interest
params_lcs_concept_hyp2_cg <- broom::tidy(fit_mi_lcs_concept_hyp2_cg, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("concept_t2 ~ concept_t1", "d_concept_1 =~ concept_t2", "d_concept_1 ~ concept_t1", # change parameters
"d_concept_1 ~1 ", "concept_t1 ~1 ", "", # means
"d_concept_1 ~~ d_concept_1")) # variances
kable(params_lcs_concept_hyp2_cg, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| concept_t2 ~ concept_t1 | 1.000 | 1.000 | 1.000 | 0.971 | NA | NA |
| d_concept_1 =~ concept_t2 | 1.000 | 1.000 | 1.000 | 0.611 | NA | NA |
| d_concept_1 ~1 | 0.580 | 0.207 | 0.954 | 1.198 | 3.045 | 0.002 |
| concept_t1 ~1 | 3.122 | 2.992 | 3.251 | 4.056 | 47.175 | 0.000 |
| d_concept_1 ~~ d_concept_1 | 0.218 | 0.097 | 0.340 | 0.929 | 3.519 | 0.000 |
| d_concept_1 ~ concept_t1 | -0.168 | -0.282 | -0.053 | -0.266 | -2.869 | 0.004 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_swls_hyp2_fu <- '
swls_t2 =~ 1*sw06_01_t2 + lamb2*sw06_02_t2 + lamb3*sw06_03_t2 + lamb4*sw06_04_t2 # This specifies the measurement model for swls_t2
swls_t3 =~ 1*sw06_01_t3 + lamb2*sw06_02_t3 + lamb3*sw06_03_t3 + lamb4*sw06_04_t3 # This specifies the measurement model for swls_t3 with the equality constrained factor loadings
swls_t3 ~ 1*swls_t2 # This parameter regresses swls_t3 perfectly on swls_t2
d_swls_1 =~ 1*swls_t3 # This defines the latent change score factor as measured perfectly by scores on swls_t3
swls_t3 ~ 0*1 # This line constrains the intercept of swls_t3 to 0
swls_t3 ~~ 0*swls_t3 # This fixes the variance of swls_t3 to 0
d_swls_1 ~ 1 # This estimates the intercept of the change score
swls_t2 ~ 1 # This estimates the intercept of swls_t2
d_swls_1 ~~ d_swls_1 # This estimates the variance of the change scores
swls_t2 ~~ swls_t2 # This estimates the variance of the swls_t2
d_swls_1 ~ swls_t2 # This estimates the self-feedback parameter
sw06_01_t2 ~~ sw06_01_t3 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t2 ~~ sw06_02_t3 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t2 ~~ sw06_03_t3 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t2 ~~ sw06_04_t3 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t2 ~~ res1*sw06_01_t2 # This allows residual variance on indicator X1 at T1
sw06_02_t2 ~~ res2*sw06_02_t2 # This allows residual variance on indicator X2 at T1
sw06_03_t2 ~~ res3*sw06_03_t2 # This allows residual variance on indicator X3 at T1
sw06_04_t2 ~~ res4*sw06_04_t2 # This allows residual variance on indicator X4 at T1
sw06_01_t3 ~~ res1*sw06_01_t3 # This allows residual variance on indicator X1 at T2
sw06_02_t3 ~~ res2*sw06_02_t3 # This allows residual variance on indicator X2 at T2
sw06_03_t3 ~~ res3*sw06_03_t3 # This allows residual variance on indicator X3 at T2
sw06_04_t3 ~~ res4*sw06_04_t3 # This allows residual variance on indicator X4 at T2
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t2 ~ m2*1 # This estimates the intercept of X2 at T1
sw06_03_t2 ~ m3*1 # This estimates the intercept of X3 at T1
sw06_04_t2 ~ m4*1 # This estimates the intercept of X4 at T1
sw06_01_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t3 ~ m2*1 # This estimates the intercept of X2 at T2
sw06_03_t3 ~ m3*1 # This estimates the intercept of X3 at T2
sw06_04_t3 ~ m4*1 # This estimates the intercept of X4 at T2
'
fit_mi_lcs_swls_hyp2_fu <- lavaan(mi_lcs_swls_hyp2_fu, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_swls_hyp2_fu, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_swls_hyp2_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 579 | 19 | 10808.9 | 10891.76 | 0.985 | 0.058 | 0.022 |
# parameters of interest
params_lcs_swls_hyp2_fu <- broom::tidy(fit_mi_lcs_swls_hyp2_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("swls_t3 ~ swls_t2", "d_swls_1 =~ swls_t3", "d_swls_1 ~ swls_t2", # change parameters
"d_swls_1 ~1 ", "swls_t2 ~1 ", "", # means
"d_swls_1 ~~ d_swls_1")) # variances
kable(params_lcs_swls_hyp2_fu, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| swls_t3 ~ swls_t2 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_swls_1 =~ swls_t3 | 1.000 | 1.000 | 1.000 | 0.532 | NA | NA |
| d_swls_1 ~1 | 0.516 | 0.306 | 0.727 | 0.952 | 4.802 | 0 |
| swls_t2 ~1 | 2.966 | 2.874 | 3.059 | 2.926 | 63.139 | 0 |
| d_swls_1 ~~ d_swls_1 | 0.275 | 0.178 | 0.372 | 0.934 | 5.551 | 0 |
| d_swls_1 ~ swls_t2 | -0.137 | -0.201 | -0.073 | -0.256 | -4.225 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_meaning_hyp2_fu <- '
meaning_t2 =~ 1*meaning_par1_t2 + lamb2*meaning_par2_t2 + lamb3*meaning_par3_t2 # This specifies the measurement model for meaning_t2
meaning_t3 =~ 1*meaning_par1_t3 + lamb2*meaning_par2_t3 + lamb3*meaning_par3_t3 # This specifies the measurement model for meaning_t3 with the equality constrained factor loadings
meaning_t3 ~ 1*meaning_t2 # This parameter regresses meaning_t3 perfectly on meaning_t2
d_meaning_1 =~ 1*meaning_t3 # This defines the latent change score factor as measured perfectly by scores on meaning_t3
meaning_t3 ~ 0*1 # This line constrains the intercept of meaning_t3 to 0
meaning_t3 ~~ 0*meaning_t3 # This fixes the variance of meaning_t3 to 0
d_meaning_1 ~ 1 # This estimates the intercept of the change score
meaning_t2 ~ 1 # This estimates the intercept of meaning_t2
d_meaning_1 ~~ d_meaning_1 # This estimates the variance of the change scores
meaning_t2 ~~ meaning_t2 # This estimates the variance of the meaning_t2
d_meaning_1 ~ meaning_t2 # This estimates the self-feedback parameter
meaning_par1_t2 ~~ meaning_par1_t3 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t2 ~~ meaning_par2_t3 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t2 ~~ meaning_par3_t3 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t2 ~~ res1*meaning_par1_t2 # This allows residual variance on indicator X1 at T1
meaning_par2_t2 ~~ res2*meaning_par2_t2 # This allows residual variance on indicator X2 at T1
meaning_par3_t2 ~~ res3*meaning_par3_t2 # This allows residual variance on indicator X3 at T1
meaning_par1_t3 ~~ res1*meaning_par1_t3 # This allows residual variance on indicator X1 at T2
meaning_par2_t3 ~~ res2*meaning_par2_t3 # This allows residual variance on indicator X2 at T2
meaning_par3_t3 ~~ res3*meaning_par3_t3 # This allows residual variance on indicator X3 at T2
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T1
meaning_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T1
meaning_par1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t3 ~ m2*1 # This estimates the intercept of X2 at T2
meaning_par3_t3 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_meaning_hyp2_fu <- lavaan(mi_lcs_meaning_hyp2_fu, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_meaning_hyp2_fu, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_meaning_hyp2_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 579 | 15 | 8150.359 | 8215.779 | 0.995 | 0.046 | 0.031 |
# parameters of interest
params_lcs_meaning_hyp2_fu <- broom::tidy(fit_mi_lcs_meaning_hyp2_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("meaning_t3 ~ meaning_t2", "d_meaning_1 =~ meaning_t3", "d_meaning_1 ~ meaning_t2", # change parameters
"d_meaning_1 ~1 ", "meaning_t2 ~1 ", "", # means
"d_meaning_1 ~~ d_meaning_1")) # variances
kable(params_lcs_meaning_hyp2_fu, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| meaning_t3 ~ meaning_t2 | 1.000 | 1.000 | 1.000 | 0.964 | NA | NA |
| d_meaning_1 =~ meaning_t3 | 1.000 | 1.000 | 1.000 | 0.674 | NA | NA |
| d_meaning_1 ~1 | 0.855 | 0.585 | 1.125 | 1.200 | 6.216 | 0 |
| meaning_t2 ~1 | 3.412 | 3.316 | 3.509 | 3.347 | 69.108 | 0 |
| d_meaning_1 ~~ d_meaning_1 | 0.463 | 0.342 | 0.585 | 0.912 | 7.467 | 0 |
| d_meaning_1 ~ meaning_t2 | -0.207 | -0.280 | -0.133 | -0.296 | -5.498 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_selfes_hyp2_fu <- '
selfes_t2 =~ 1*selfes_par1_t2 + lamb2*selfes_par2_t2 + lamb3*selfes_par3_t2 # This specifies the measurement model for selfes_t2
selfes_t3 =~ 1*selfes_par1_t3 + lamb2*selfes_par2_t3 + lamb3*selfes_par3_t3 # This specifies the measurement model for selfes_t3 with the equality constrained factor loadings
selfes_t3 ~ 1*selfes_t2 # This parameter regresses selfes_t3 perfectly on selfes_t2
d_selfes_1 =~ 1*selfes_t3 # This defines the latent change score factor as measured perfectly by scores on selfes_t3
selfes_t3 ~ 0*1 # This line constrains the intercept of selfes_t3 to 0
selfes_t3 ~~ 0*selfes_t3 # This fixes the variance of selfes_t3 to 0
d_selfes_1 ~ 1 # This estimates the intercept of the change score
selfes_t2 ~ 1 # This estimates the intercept of selfes_t2
d_selfes_1 ~~ d_selfes_1 # This estimates the variance of the change scores
selfes_t2 ~~ selfes_t2 # This estimates the variance of the selfes_t2
d_selfes_1 ~ selfes_t2 # This estimates the self-feedback parameter
selfes_par1_t2 ~~ selfes_par1_t3 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t2 ~~ selfes_par2_t3 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t2 ~~ selfes_par3_t3 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t2 ~~ res1*selfes_par1_t2 # This allows residual variance on indicator X1 at T1
selfes_par2_t2 ~~ res2*selfes_par2_t2 # This allows residual variance on indicator X2 at T1
selfes_par3_t2 ~~ res3*selfes_par3_t2 # This allows residual variance on indicator X3 at T1
selfes_par1_t3 ~~ res1*selfes_par1_t3 # This allows residual variance on indicator X1 at T2
selfes_par2_t3 ~~ res2*selfes_par2_t3 # This allows residual variance on indicator X2 at T2
selfes_par3_t3 ~~ res3*selfes_par3_t3 # This allows residual variance on indicator X3 at T2
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T1
selfes_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T1
selfes_par1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t3 ~ m2*1 # This estimates the intercept of X2 at T2
selfes_par3_t3 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_selfes_hyp2_fu <- lavaan(mi_lcs_selfes_hyp2_fu, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_selfes_hyp2_fu, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_selfes_hyp2_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 579 | 15 | 5982.84 | 6048.26 | 1 | 0 | 0.015 |
# parameters of interest
params_lcs_selfes_hyp2_fu <- broom::tidy(fit_mi_lcs_selfes_hyp2_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("selfes_t3 ~ selfes_t2", "d_selfes_1 =~ selfes_t3", "d_selfes_1 ~ selfes_t2", # change parameters
"d_selfes_1 ~1 ", "selfes_t2 ~1 ", "", # means
"d_selfes_1 ~~ d_selfes_1")) # variances
kable(params_lcs_selfes_hyp2_fu, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| selfes_t3 ~ selfes_t2 | 1.000 | 1.000 | 1.000 | 0.973 | NA | NA |
| d_selfes_1 =~ selfes_t3 | 1.000 | 1.000 | 1.000 | 0.499 | NA | NA |
| d_selfes_1 ~1 | 0.445 | 0.234 | 0.656 | 1.213 | 4.138 | 0 |
| selfes_t2 ~1 | 3.814 | 3.748 | 3.880 | 5.329 | 113.354 | 0 |
| d_selfes_1 ~~ d_selfes_1 | 0.129 | 0.099 | 0.160 | 0.960 | 8.377 | 0 |
| d_selfes_1 ~ selfes_t2 | -0.103 | -0.155 | -0.051 | -0.201 | -3.870 | 0 |
Fit model:
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007
# Fit the multiple indicator Univariate Latent Change Score model
mi_lcs_concept_hyp2_fu <- '
concept_t2 =~ 1*concept_par1_t2 + lamb2*concept_par2_t2 + lamb3*concept_par3_t2 # This specifies the measurement model for concept_t2
concept_t3 =~ 1*concept_par1_t3 + lamb2*concept_par2_t3 + lamb3*concept_par3_t3 # This specifies the measurement model for concept_t3 with the equality constrained factor loadings
concept_t3 ~ 1*concept_t2 # This parameter regresses concept_t3 perfectly on concept_t2
d_concept_1 =~ 1*concept_t3 # This defines the latent change score factor as measured perfectly by scores on concept_t3
concept_t3 ~ 0*1 # This line constrains the intercept of concept_t3 to 0
concept_t3 ~~ 0*concept_t3 # This fixes the variance of concept_t3 to 0
d_concept_1 ~ 1 # This estimates the intercept of the change score
concept_t2 ~ 1 # This estimates the intercept of concept_t2
d_concept_1 ~~ d_concept_1 # This estimates the variance of the change scores
concept_t2 ~~ concept_t2 # This estimates the variance of the concept_t2
d_concept_1 ~ concept_t2 # This estimates the self-feedback parameter
concept_par1_t2 ~~ concept_par1_t3 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t2 ~~ concept_par2_t3 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t2 ~~ concept_par3_t3 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t2 ~~ res1*concept_par1_t2 # This allows residual variance on indicator X1 at T1
concept_par2_t2 ~~ res2*concept_par2_t2 # This allows residual variance on indicator X2 at T1
concept_par3_t2 ~~ res3*concept_par3_t2 # This allows residual variance on indicator X3 at T1
concept_par1_t3 ~~ res1*concept_par1_t3 # This allows residual variance on indicator X1 at T2
concept_par2_t3 ~~ res2*concept_par2_t3 # This allows residual variance on indicator X2 at T2
concept_par3_t3 ~~ res3*concept_par3_t3 # This allows residual variance on indicator X3 at T2
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T1
concept_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T1
concept_par1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t3 ~ m2*1 # This estimates the intercept of X2 at T2
concept_par3_t3 ~ m3*1 # This estimates the intercept of X3 at T2
'
fit_mi_lcs_concept_hyp2_fu <- lavaan(mi_lcs_concept_hyp2_fu, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_concept_hyp2_fu, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary:
# model fit
kable(broom::glance(fit_mi_lcs_concept_hyp2_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 579 | 15 | 6175.822 | 6241.241 | 0.996 | 0.042 | 0.019 |
# parameters of interest
params_lcs_concept_hyp2_fu <- broom::tidy(fit_mi_lcs_concept_hyp2_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("concept_t3 ~ concept_t2", "d_concept_1 =~ concept_t3", "d_concept_1 ~ concept_t2", # change parameters
"d_concept_1 ~1 ", "concept_t2 ~1 ", "", # means
"d_concept_1 ~~ d_concept_1")) # variances
kable(params_lcs_concept_hyp2_fu, digits = 3)| term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| concept_t3 ~ concept_t2 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_concept_1 =~ concept_t3 | 1.000 | 1.000 | 1.000 | 0.600 | NA | NA |
| d_concept_1 ~1 | 0.555 | 0.352 | 0.758 | 1.407 | 5.357 | 0 |
| concept_t2 ~1 | 3.226 | 3.163 | 3.288 | 5.063 | 101.242 | 0 |
| d_concept_1 ~~ d_concept_1 | 0.145 | 0.111 | 0.179 | 0.934 | 8.418 | 0 |
| d_concept_1 ~ concept_t2 | -0.159 | -0.218 | -0.099 | -0.257 | -5.243 | 0 |
The distance between ideal- and current-self will decrease in both intervention groups (but not in the control group between T1 and T2).
We will use two strategies to test this hypothesis. First, we will compute the Fisher z-transformed profile correlation between current- and ideal-self and test whether it increased across assessments. Second, we will test whether the squared difference between current- and ideal-self rating for each Big Five trait decreased across assessments. We will test mean-level differences in profile correlations and squared differences between baseline and follow up using mixed-effects models. Our main focus will be changes from T1 to T2 and we will check in a second set of models whether any changes persist from T2 to T3.
Reshape to wide:
# reshape to wide
df_sbsa2_wide_profdiff <- df_sbsa2 %>%
arrange(pid, time) %>%
select(pid, rando, time, profile_corr_item_z, profile_corr_facet_z, ends_with("_sqdiff")) %>%
pivot_wider(names_from = time,
names_sep = "_t",
values_from = -c(pid, rando, time))Checking assumptions
# item-level profile correlation
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t1)
W = 0.93322, p-value = 2.657e-15lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t1)
D = 0.078375, p-value = 6.384e-09# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 671 0.42 0.49 0.38 0.39 0.47 -1.09 3.39 4.48 0.81 2.34 0.02psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 575 0.56 0.49 0.53 0.54 0.51 -1.07 2.17 3.23 0.27 0.02 0.02# facet-level profile correlation
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t1)
W = 0.93724, p-value = 8.082e-15lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t1)
D = 0.080938, p-value = 1.393e-09# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 672 0.35 0.66 0.29 0.31 0.64 -1.23 3.77 5 0.69 1.04 0.03psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 576 0.52 0.66 0.47 0.49 0.66 -1.34 3.09 4.43 0.37 0.02 0.03Results summary:
t_profile_corr_item <- t.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2),
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t1),
paired = TRUE)
t_profile_corr_facet <- t.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2),
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t1),
paired = TRUE)
kable(bind_rows(tidy(t_profile_corr_item), tidy(t_profile_corr_facet)) %>% mutate(outcome = c("Item-level profile corr.", "Facet-level profile corr.")) %>% select(outcome, everything()), digits = 3)| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| Item-level profile corr. | 0.131 | 8.684 | 0 | 571 | 0.101 | 0.160 | Paired t-test | two.sided |
| Facet-level profile corr. | 0.169 | 8.395 | 0 | 573 | 0.129 | 0.208 | Paired t-test | two.sided |
# effect sizes
d_profile_corr_item <- psych::cohen.d(profile_corr_item_z ~ time, data = df_sbsa2 %>%
filter(rando!="Waitlist-Control" & time %in% c(1,2)))
d_profile_corr_item$cohen.d lower effect upper
profile_corr_item_z 0.1668914 0.2788659 0.3907299d_profile_corr_facet <- psych::cohen.d(profile_corr_facet_z ~ time, data = df_sbsa2 %>%
filter(rando!="Waitlist-Control" & time %in% c(1,2)))
d_profile_corr_facet$cohen.d lower effect upper
profile_corr_facet_z 0.1475916 0.2593992 0.3711035What happens in the waitlist control group during this time?
t_profile_corr_item_cg <- t.test(df_sbsa2_wide_profdiff %>% filter(rando=="Waitlist-Control") %>% pull(profile_corr_item_z_t2),
df_sbsa2_wide_profdiff %>% filter(rando=="Waitlist-Control") %>% pull(profile_corr_item_z_t1),
paired = TRUE)
t_profile_corr_facet_cg <- t.test(df_sbsa2_wide_profdiff %>% filter(rando=="Waitlist-Control") %>% pull(profile_corr_facet_z_t2),
df_sbsa2_wide_profdiff %>% filter(rando=="Waitlist-Control") %>% pull(profile_corr_facet_z_t1),
paired = TRUE)
kable(bind_rows(tidy(t_profile_corr_item_cg), tidy(t_profile_corr_facet_cg)) %>% mutate(outcome = c("Item-level profile corr.", "Facet-level profile corr.")) %>% select(outcome, everything()), digits = 5)| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| Item-level profile corr. | 0.10158 | 3.36943 | 0.00097 | 145 | 0.0420 | 0.16117 | Paired t-test | two.sided |
| Facet-level profile corr. | 0.12900 | 3.24385 | 0.00146 | 145 | 0.0504 | 0.20760 | Paired t-test | two.sided |
# effect sizes
d_profile_corr_item_cg <- psych::cohen.d(profile_corr_item_z ~ time, data = df_sbsa2 %>%
filter(rando=="Waitlist-Control" & time %in% c(1,2)))
d_profile_corr_item_cg$cohen.d lower effect upper
profile_corr_item_z -0.02087351 0.1993958 0.4193518d_profile_corr_facet_cg <- psych::cohen.d(profile_corr_facet_z ~ time, data = df_sbsa2 %>%
filter(rando=="Waitlist-Control" & time %in% c(1,2)))
d_profile_corr_facet_cg$cohen.d lower effect upper
profile_corr_facet_z -0.04429333 0.1758341 0.3956873Checking assumptions
# item-level profile correlation
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t2)
W = 0.91279, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_item_z_t2)
D = 0.062819, p-value = 5.247e-05# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 575 0.56 0.49 0.53 0.54 0.51 -1.07 2.17 3.23 0.27 0.02 0.02psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 0.61 0.55 0.57 0.59 0.55 -1.22 3.34 4.56 0.48 1.4 0.02# facet-level profile correlation
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t2)
W = 0.92574, p-value = 3.207e-15lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(profile_corr_facet_z_t2)
D = 0.067574, p-value = 7.904e-06# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 576 0.52 0.66 0.47 0.49 0.66 -1.34 3.09 4.43 0.37 0.02 0.03psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 0.59 0.72 0.54 0.56 0.71 -1.7 3.72 5.42 0.47 0.54 0.03Results summary:
t_profile_corr_item_fu <- t.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t3),
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_item_z_t2),
paired = TRUE)
t_profile_corr_facet_fu <- t.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t3),
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(profile_corr_facet_z_t2),
paired = TRUE)
kable(bind_rows(tidy(t_profile_corr_item_fu), tidy(t_profile_corr_facet_fu)) %>% mutate(outcome = c("Item-level profile corr.", "Facet-level profile corr.")) %>% select(outcome, everything()), digits = 3)| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| Item-level profile corr. | 0.040 | 2.583 | 0.010 | 511 | 0.010 | 0.071 | Paired t-test | two.sided |
| Facet-level profile corr. | 0.066 | 3.097 | 0.002 | 511 | 0.024 | 0.108 | Paired t-test | two.sided |
# effect sizes
d_profile_corr_item_fu <- psych::cohen.d(profile_corr_item_z ~ time, data = df_sbsa2 %>%
filter(rando!="Waitlist-Control" & time %in% c(2,3)))
d_profile_corr_item_fu$cohen.d lower effect upper
profile_corr_item_z -0.01900003 0.1000698 0.2190937d_profile_corr_facet_fu <- psych::cohen.d(profile_corr_facet_z ~ time, data = df_sbsa2 %>%
filter(rando!="Waitlist-Control" & time %in% c(2,3)))
d_profile_corr_facet_fu$cohen.d lower effect upper
profile_corr_facet_z -0.01257597 0.1064569 0.2254401profile_df_plot <- df_sbsa2 %>%
select(pid, time, rando, profile_corr_item_z, profile_corr_facet_z) %>%
pivot_longer(-c(pid, time, rando), names_to = "itemfacet", values_to = "corr") %>%
filter(!is.na(corr)) %>%
mutate(itemfacet2 = fct_recode(itemfacet, "Item-level" = "profile_corr_item_z", "Facet-level" = "profile_corr_facet_z"),
itemfacet2 = fct_reorder(itemfacet2, corr, .desc = F))
cowplot::plot_grid(
ggplot(profile_df_plot %>% filter(rando!="Waitlist-Control")) +
aes(x = as.factor(time), y = corr) +
geom_boxplot() +
geom_violin(fill = NA) +
facet_wrap(vars(itemfacet2)) +
labs(x = "Measurement Occasion", y = "Profile correlation",
title = "Distance between ideal- and current-self in intervention groups") +
theme_bw(),
ggplot(profile_df_plot %>% filter(rando=="Waitlist-Control")) +
aes(x = as.factor(time), y = corr) +
geom_boxplot() +
geom_violin(fill = NA) +
facet_wrap(vars(itemfacet2)) +
labs(x = "Measurement Occasion", y = "Profile correlation",
title = "Distance between ideal- and current-self in waitlist control group") +
theme_bw(),
ncol = 1, align = "vt"
)
Checking assumptions
# extra
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t1)
W = 0.90382, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t1)
D = 0.12962, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 674 1.84 1.98 1.17 1.5 1.48 0 11.67 11.67 1.57 2.63 0.08psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 1.47 1.71 0.84 1.14 1.07 0 10.03 10.03 1.87 3.83 0.07# agree
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t1)
W = 0.75936, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t1)
D = 0.16923, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 673 0.5 0.81 0.17 0.33 0.25 0 7.56 7.56 3.64 20.07 0.03psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 0.39 0.83 0.11 0.24 0.16 0 12.84 12.84 7.93 97.34 0.03# consc
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t1)
W = 0.87992, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t1)
D = 0.12582, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 674 1.7 2.03 1 1.31 1.32 0 16 16 2.08 6.1 0.08psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 1.46 1.85 0.84 1.1 1.15 0 14.06 14.06 2.19 6.64 0.08# neuro
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t1)
W = 0.88583, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t1)
D = 0.14624, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 674 3.36 3.44 2.25 2.79 2.68 0 16 16 1.34 1.27 0.13psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 2.66 2.97 1.56 2.11 1.95 0 16 16 1.64 2.6 0.12# openn
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t1))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t1)
W = 0.63533, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t2) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t1))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t2) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t1)
D = 0.19368, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t1)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 673 0.47 0.92 0.11 0.27 0.16 0 9.51 9.51 4.59 29.24 0.04psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 576 0.39 0.69 0.11 0.23 0.16 0 6.25 6.25 4.03 22.73 0.03Results summary:
mod_traits_sqdiff <- df_sbsa2_wide_profdiff %>%
filter(rando!="Waitlist-Control") %>%
select(-starts_with("profile"), -rando, -ends_with("_t3")) %>%
pivot_longer(-c(pid),
names_to = c("test", "time"), values_to = "score",
names_pattern = "(.*)_(t1|t2)") %>%
filter(!is.na(score)) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(t_tests = map(data, ~t.test(score ~ relevel(as.factor(time), "t2"), # need to relevel this to get T2-T1 diff (not sure why)
data = .x, paired = TRUE))) %>%
pull(t_tests) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
mod_traits_unlist <- mod_traits_sqdiff[[1]] %>% tidy()
for (i in 2:length(mod_traits_sqdiff)) {
mod_traits_unlist <- bind_rows(mod_traits_unlist, mod_traits_sqdiff[[i]] %>% tidy())
}
kable(mod_traits_unlist %>% mutate(outcome = sort(names(b5_vars))) %>%
select(outcome, everything()) %>% arrange(factor(outcome, levels = (names(b5_vars)))), digits = 3) # order by BFI traits| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| extraversion | -0.372 | -6.354 | 0.000 | 576 | -0.487 | -0.257 | Paired t-test | two.sided |
| agreeableness | -0.097 | -3.536 | 0.000 | 575 | -0.151 | -0.043 | Paired t-test | two.sided |
| conscientiousness | -0.169 | -2.890 | 0.004 | 576 | -0.284 | -0.054 | Paired t-test | two.sided |
| neuroticism | -0.697 | -6.865 | 0.000 | 576 | -0.896 | -0.498 | Paired t-test | two.sided |
| openness | -0.076 | -2.587 | 0.010 | 574 | -0.134 | -0.018 | Paired t-test | two.sided |
| sociability | -0.782 | -7.573 | 0.000 | 574 | -0.985 | -0.579 | Paired t-test | two.sided |
| assertiveness | -0.239 | -3.030 | 0.003 | 573 | -0.394 | -0.084 | Paired t-test | two.sided |
| energy | -0.337 | -3.541 | 0.000 | 575 | -0.523 | -0.150 | Paired t-test | two.sided |
| compassion | -0.118 | -2.769 | 0.006 | 575 | -0.201 | -0.034 | Paired t-test | two.sided |
| respectfulness | -0.012 | -0.321 | 0.749 | 572 | -0.086 | 0.062 | Paired t-test | two.sided |
| trust | -0.329 | -4.856 | 0.000 | 574 | -0.462 | -0.196 | Paired t-test | two.sided |
| organization | -0.149 | -1.777 | 0.076 | 575 | -0.314 | 0.016 | Paired t-test | two.sided |
| productiveness | -0.453 | -4.519 | 0.000 | 572 | -0.650 | -0.256 | Paired t-test | two.sided |
| responsibility | -0.139 | -2.065 | 0.039 | 573 | -0.271 | -0.007 | Paired t-test | two.sided |
| anxiety | -0.681 | -4.592 | 0.000 | 576 | -0.972 | -0.389 | Paired t-test | two.sided |
| depression | -0.887 | -7.570 | 0.000 | 574 | -1.117 | -0.657 | Paired t-test | two.sided |
| volatility | -0.603 | -4.782 | 0.000 | 576 | -0.851 | -0.355 | Paired t-test | two.sided |
| curiosity | -0.044 | -1.039 | 0.299 | 573 | -0.128 | 0.040 | Paired t-test | two.sided |
| aesthetic | -0.053 | -1.359 | 0.175 | 572 | -0.130 | 0.024 | Paired t-test | two.sided |
| imagination | -0.215 | -3.559 | 0.000 | 574 | -0.334 | -0.097 | Paired t-test | two.sided |
# effect sizes
cohend_cust = function(outcome, df) {
form = as.formula(paste0(outcome, " ~ time"))
psych::cohen.d(form, data = df)
}
d_trait <- cohend_cust(paste0(str_trunc(names(b5_vars)[1], 5, ellipsis = ""), "_sqdiff"),
df_sbsa2 %>% filter(rando!="Waitlist-Control" & time %in% c(1,2)))
d_traits_unlist <- as_tibble(d_trait$cohen.d)
for (i in 2:length(names(b5_vars))) {
d_trait <- cohend_cust(paste0(str_trunc(names(b5_vars)[i], 5, ellipsis = ""), "_sqdiff"),
df_sbsa2 %>% filter(rando!="Waitlist-Control" & time %in% c(1,2)))
d_traits_unlist <- bind_rows(d_traits_unlist, as_tibble(d_trait$cohen.d))
}
kable(d_traits_unlist %>% mutate(outcome = names(b5_vars)) %>% select(outcome, everything()), digits = 3)| outcome | lower | effect | upper |
|---|---|---|---|
| extraversion | -0.310 | -0.199 | -0.088 |
| agreeableness | -0.247 | -0.135 | -0.024 |
| conscientiousness | -0.235 | -0.124 | -0.013 |
| neuroticism | -0.328 | -0.217 | -0.105 |
| openness | -0.212 | -0.101 | 0.010 |
| sociability | -0.345 | -0.233 | -0.122 |
| assertiveness | -0.219 | -0.108 | 0.003 |
| energy | -0.268 | -0.157 | -0.045 |
| compassion | -0.231 | -0.120 | -0.008 |
| respectfulness | -0.173 | -0.062 | 0.050 |
| trust | -0.303 | -0.191 | -0.080 |
| organization | -0.202 | -0.091 | 0.020 |
| productiveness | -0.286 | -0.174 | -0.063 |
| responsibility | -0.223 | -0.112 | 0.000 |
| anxiety | -0.272 | -0.160 | -0.049 |
| depression | -0.351 | -0.240 | -0.128 |
| volatility | -0.283 | -0.172 | -0.060 |
| curiosity | -0.161 | -0.049 | 0.062 |
| aesthetic | -0.178 | -0.066 | 0.045 |
| imagination | -0.235 | -0.124 | -0.012 |
What happens in the waitlist control group during this time?
mod_traits_sqdiff_cg <- df_sbsa2_wide_profdiff %>%
filter(rando=="Waitlist-Control") %>%
select(-starts_with("profile"), -rando, -ends_with("_t3")) %>%
pivot_longer(-c(pid),
names_to = c("test", "time"), values_to = "score",
names_pattern = "(.*)_(t1|t2)") %>%
filter(!is.na(score)) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(t_tests = map(data, ~t.test(score ~ relevel(as.factor(time), "t2"), # need to relevel this to get T2-T1 diff (not sure why)
data = .x, paired = TRUE))) %>%
pull(t_tests) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
mod_traits_unlist_cg <- mod_traits_sqdiff_cg[[1]] %>% tidy()
for (i in 2:length(mod_traits_sqdiff_cg)) {
mod_traits_unlist_cg <- bind_rows(mod_traits_unlist_cg, mod_traits_sqdiff_cg[[i]] %>% tidy())
}
kable(mod_traits_unlist_cg %>% mutate(outcome = sort(names(b5_vars))) %>%
select(outcome, everything()) %>% arrange(factor(outcome, levels = (names(b5_vars)))), digits = 3) # order by BFI traits| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| extraversion | -0.371 | -2.622 | 0.010 | 145 | -0.650 | -0.091 | Paired t-test | two.sided |
| agreeableness | -0.158 | -2.381 | 0.019 | 145 | -0.290 | -0.027 | Paired t-test | two.sided |
| conscientiousness | -0.347 | -3.151 | 0.002 | 145 | -0.565 | -0.130 | Paired t-test | two.sided |
| neuroticism | -0.600 | -3.067 | 0.003 | 145 | -0.986 | -0.213 | Paired t-test | two.sided |
| openness | -0.052 | -1.042 | 0.299 | 145 | -0.151 | 0.047 | Paired t-test | two.sided |
| sociability | -0.451 | -2.337 | 0.021 | 145 | -0.833 | -0.070 | Paired t-test | two.sided |
| assertiveness | -0.375 | -1.927 | 0.056 | 145 | -0.759 | 0.010 | Paired t-test | two.sided |
| energy | -0.495 | -2.212 | 0.029 | 145 | -0.937 | -0.053 | Paired t-test | two.sided |
| compassion | -0.268 | -2.470 | 0.015 | 145 | -0.482 | -0.053 | Paired t-test | two.sided |
| respectfulness | -0.092 | -1.222 | 0.224 | 145 | -0.242 | 0.057 | Paired t-test | two.sided |
| trust | -0.213 | -1.634 | 0.104 | 145 | -0.470 | 0.045 | Paired t-test | two.sided |
| organization | -0.556 | -3.541 | 0.001 | 145 | -0.867 | -0.246 | Paired t-test | two.sided |
| productiveness | -0.544 | -3.182 | 0.002 | 145 | -0.882 | -0.206 | Paired t-test | two.sided |
| responsibility | -0.304 | -2.500 | 0.014 | 145 | -0.545 | -0.064 | Paired t-test | two.sided |
| anxiety | -0.625 | -2.144 | 0.034 | 145 | -1.200 | -0.049 | Paired t-test | two.sided |
| depression | -0.745 | -3.091 | 0.002 | 145 | -1.222 | -0.269 | Paired t-test | two.sided |
| volatility | -0.596 | -2.357 | 0.020 | 145 | -1.095 | -0.096 | Paired t-test | two.sided |
| curiosity | -0.068 | -1.089 | 0.278 | 145 | -0.190 | 0.055 | Paired t-test | two.sided |
| aesthetic | -0.036 | -0.363 | 0.717 | 145 | -0.232 | 0.160 | Paired t-test | two.sided |
| imagination | -0.074 | -0.569 | 0.570 | 145 | -0.329 | 0.182 | Paired t-test | two.sided |
# effect sizes
d_trait_cg <- cohend_cust(paste0(str_trunc(names(b5_vars)[1], 5, ellipsis = ""), "_sqdiff"),
df_sbsa2 %>% filter(rando=="Waitlist-Control" & time %in% c(1,2)))
d_traits_unlist_cg <- as_tibble(d_trait_cg$cohen.d)
for (i in 2:length(names(b5_vars))) {
d_trait_cg <- cohend_cust(paste0(str_trunc(names(b5_vars)[i], 5, ellipsis = ""), "_sqdiff"),
df_sbsa2 %>% filter(rando=="Waitlist-Control" & time %in% c(1,2)))
d_traits_unlist_cg <- bind_rows(d_traits_unlist_cg, as_tibble(d_trait_cg$cohen.d))
}
kable(d_traits_unlist_cg %>% mutate(outcome = names(b5_vars)) %>% select(outcome, everything()), digits = 3)| outcome | lower | effect | upper |
|---|---|---|---|
| extraversion | -0.415 | -0.195 | 0.025 |
| agreeableness | -0.410 | -0.190 | 0.030 |
| conscientiousness | -0.434 | -0.214 | 0.007 |
| neuroticism | -0.380 | -0.160 | 0.060 |
| openness | -0.310 | -0.090 | 0.130 |
| sociability | -0.388 | -0.169 | 0.051 |
| assertiveness | -0.366 | -0.146 | 0.074 |
| energy | -0.410 | -0.191 | 0.030 |
| compassion | -0.480 | -0.260 | -0.039 |
| respectfulness | -0.337 | -0.118 | 0.102 |
| trust | -0.296 | -0.076 | 0.143 |
| organization | -0.436 | -0.216 | 0.004 |
| productiveness | -0.405 | -0.185 | 0.035 |
| responsibility | -0.426 | -0.206 | 0.014 |
| anxiety | -0.355 | -0.135 | 0.085 |
| depression | -0.386 | -0.166 | 0.054 |
| volatility | -0.354 | -0.134 | 0.086 |
| curiosity | -0.322 | -0.102 | 0.118 |
| aesthetic | -0.298 | -0.079 | 0.141 |
| imagination | -0.261 | -0.042 | 0.178 |
Checking assumptions
# extra
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t2)
W = 0.91718, p-value = 3.875e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(extra_sqdiff_t2)
D = 0.12883, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 1.47 1.71 0.84 1.14 1.07 0 10.03 10.03 1.87 3.83 0.07psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(extra_sqdiff_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 1.37 1.65 0.69 1.06 0.94 0 9 9 1.84 3.81 0.07# agree
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t2)
W = 0.70258, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(agree_sqdiff_t2)
D = 0.17613, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 0.39 0.83 0.11 0.24 0.16 0 12.84 12.84 7.93 97.34 0.03psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(agree_sqdiff_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 0.36 0.68 0.11 0.23 0.16 0 9 9 6.09 59.52 0.03# consc
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t2)
W = 0.81404, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(consc_sqdiff_t2)
D = 0.15513, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 1.46 1.85 0.84 1.1 1.15 0 14.06 14.06 2.19 6.64 0.08psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(consc_sqdiff_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 1.35 1.81 0.69 0.98 0.96 0 11.67 11.67 2.39 7 0.08# neuro
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t2)
W = 0.9264, p-value = 3.804e-15lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(neuro_sqdiff_t2)
D = 0.13798, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 577 2.66 2.97 1.56 2.11 1.95 0 16 16 1.64 2.6 0.12psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(neuro_sqdiff_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 2.44 2.76 1.36 1.95 1.7 0 16 16 1.83 4.04 0.12# openn
shapiro.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t2))
Shapiro-Wilk normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t2)
W = 0.7161, p-value < 2.2e-16lillie.test(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t3) -
df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t2))
Lilliefors (Kolmogorov-Smirnov) normality test
data: df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t3) - df_sbsa2_wide_profdiff %>% filter(rando != "Waitlist-Control") %>% pull(openn_sqdiff_t2)
D = 0.19517, p-value < 2.2e-16# descriptives
psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t2)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 576 0.39 0.69 0.11 0.23 0.16 0 6.25 6.25 4.03 22.73 0.03psych::describe(df_sbsa2_wide_profdiff %>% filter(rando!="Waitlist-Control") %>% pull(openn_sqdiff_t3)) vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 514 0.42 0.9 0.11 0.23 0.16 0 11.67 11.67 6.54 62.09 0.04Results summary:
mod_traits_sqdiff_fu <- df_sbsa2_wide_profdiff %>%
filter(rando!="Waitlist-Control") %>%
select(-starts_with("profile"), -rando, -ends_with("_t1")) %>%
pivot_longer(-c(pid),
names_to = c("test", "time"), values_to = "score",
names_pattern = "(.*)_(t2|t3)") %>%
filter(!is.na(score)) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(t_tests = map(data, ~t.test(score ~ relevel(as.factor(time), "t3"), # need to relevel this to get T3-T2 diff (not sure why)
data = .x, paired = TRUE))) %>%
pull(t_tests) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
mod_traits_unlist_fu <- mod_traits_sqdiff_fu[[1]] %>% tidy()
for (i in 2:length(mod_traits_sqdiff_fu)) {
mod_traits_unlist_fu <- bind_rows(mod_traits_unlist_fu, mod_traits_sqdiff_fu[[i]] %>% tidy())
}
kable(mod_traits_unlist_fu %>% mutate(outcome = sort(names(b5_vars))) %>%
select(outcome, everything()) %>% arrange(factor(outcome, levels = (names(b5_vars)))), digits = 3) # order by BFI traits| outcome | estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|
| extraversion | -0.096 | -1.820 | 0.069 | 511 | -0.199 | 0.008 | Paired t-test | two.sided |
| agreeableness | -0.007 | -0.304 | 0.761 | 511 | -0.053 | 0.038 | Paired t-test | two.sided |
| conscientiousness | -0.086 | -1.451 | 0.147 | 511 | -0.203 | 0.031 | Paired t-test | two.sided |
| neuroticism | -0.205 | -2.573 | 0.010 | 511 | -0.362 | -0.049 | Paired t-test | two.sided |
| openness | 0.030 | 1.064 | 0.288 | 511 | -0.025 | 0.085 | Paired t-test | two.sided |
| sociability | -0.138 | -1.437 | 0.151 | 511 | -0.326 | 0.050 | Paired t-test | two.sided |
| assertiveness | -0.121 | -1.553 | 0.121 | 511 | -0.273 | 0.032 | Paired t-test | two.sided |
| energy | -0.001 | -0.007 | 0.995 | 511 | -0.176 | 0.174 | Paired t-test | two.sided |
| compassion | 0.003 | 0.083 | 0.934 | 511 | -0.072 | 0.078 | Paired t-test | two.sided |
| respectfulness | -0.032 | -0.937 | 0.349 | 511 | -0.098 | 0.035 | Paired t-test | two.sided |
| trust | 0.005 | 0.097 | 0.923 | 511 | -0.101 | 0.112 | Paired t-test | two.sided |
| organization | -0.095 | -1.083 | 0.279 | 511 | -0.266 | 0.077 | Paired t-test | two.sided |
| productiveness | -0.139 | -1.506 | 0.133 | 511 | -0.321 | 0.043 | Paired t-test | two.sided |
| responsibility | -0.057 | -0.838 | 0.403 | 511 | -0.190 | 0.077 | Paired t-test | two.sided |
| anxiety | -0.380 | -2.893 | 0.004 | 511 | -0.638 | -0.122 | Paired t-test | two.sided |
| depression | -0.130 | -1.138 | 0.256 | 511 | -0.356 | 0.095 | Paired t-test | two.sided |
| volatility | -0.163 | -1.670 | 0.095 | 511 | -0.355 | 0.029 | Paired t-test | two.sided |
| curiosity | 0.040 | 0.904 | 0.366 | 511 | -0.047 | 0.127 | Paired t-test | two.sided |
| aesthetic | 0.083 | 1.791 | 0.074 | 511 | -0.008 | 0.175 | Paired t-test | two.sided |
| imagination | -0.101 | -1.670 | 0.096 | 511 | -0.220 | 0.018 | Paired t-test | two.sided |
# effect sizes
d_trait_fu <- cohend_cust(paste0(str_trunc(names(b5_vars)[1], 5, ellipsis = ""), "_sqdiff"),
df_sbsa2 %>% filter(rando!="Waitlist-Control" & time %in% c(2,3)))
d_traits_unlist_fu <- as_tibble(d_trait_fu$cohen.d)
for (i in 2:length(names(b5_vars))) {
d_trait_fu <- cohend_cust(paste0(str_trunc(names(b5_vars)[i], 5, ellipsis = ""), "_sqdiff"),
df_sbsa2 %>% filter(rando!="Waitlist-Control" & time %in% c(2,3)))
d_traits_unlist_fu <- bind_rows(d_traits_unlist_fu, as_tibble(d_trait_fu$cohen.d))
}
kable(d_traits_unlist_fu %>% mutate(outcome = names(b5_vars)) %>% select(outcome, everything()), digits = 3)| outcome | lower | effect | upper |
|---|---|---|---|
| extraversion | -0.179 | -0.060 | 0.059 |
| agreeableness | -0.155 | -0.037 | 0.082 |
| conscientiousness | -0.178 | -0.059 | 0.060 |
| neuroticism | -0.194 | -0.075 | 0.044 |
| openness | -0.086 | 0.033 | 0.152 |
| sociability | -0.163 | -0.044 | 0.075 |
| assertiveness | -0.199 | -0.080 | 0.039 |
| energy | -0.119 | 0.000 | 0.119 |
| compassion | -0.149 | -0.030 | 0.089 |
| respectfulness | -0.185 | -0.066 | 0.053 |
| trust | -0.116 | 0.002 | 0.121 |
| organization | -0.173 | -0.054 | 0.065 |
| productiveness | -0.175 | -0.056 | 0.063 |
| responsibility | -0.156 | -0.037 | 0.082 |
| anxiety | -0.217 | -0.098 | 0.021 |
| depression | -0.154 | -0.035 | 0.084 |
| volatility | -0.180 | -0.061 | 0.058 |
| curiosity | -0.107 | 0.012 | 0.131 |
| aesthetic | -0.043 | 0.076 | 0.195 |
| imagination | -0.186 | -0.067 | 0.052 |
Traits/facets sorted by average size of squared differences - in the intervention groups from T1 to T2
sqdiff_df_plot <- df_sbsa2 %>%
filter(rando!="Waitlist-Control" & time %in% c(1,2)) %>%
select(pid, time, all_of(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"))) %>%
pivot_longer(-c(pid, time), names_to = "trait", values_to = "sqdiff") %>%
group_by(trait) %>%
mutate(cur_group_id()) %>%
ungroup() %>%
filter(!is.na(sqdiff)) %>%
mutate(trait2 = fct_recode(trait, !!! setNames(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"),
str_to_title(names(b5_vars)))),
trait2 = fct_reorder(trait2, sqdiff, .desc = T))
plot_sqdiff <- ggplot(sqdiff_df_plot) +
aes(x = as.factor(time), y = sqdiff) +
geom_boxplot() +
geom_violin(fill = NA) +
ggforce::facet_wrap_paginate(~trait2, ncol=3, nrow=2) +
labs(x = "Measurement Occasion", y = "Squared difference") +
theme_bw()
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 1)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 2)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 3)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 4)
plot_sqdiff + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 5)
Traits/facets sorted by average size of squared differences - in the control group from T1 to T2
sqdiff_df_plot_cg <- df_sbsa2 %>%
filter(rando=="Waitlist-Control" & time %in% c(1,2)) %>%
select(pid, time, all_of(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"))) %>%
pivot_longer(-c(pid, time), names_to = "trait", values_to = "sqdiff") %>%
group_by(trait) %>%
mutate(cur_group_id()) %>%
ungroup() %>%
filter(!is.na(sqdiff)) %>%
mutate(trait2 = fct_recode(trait, !!! setNames(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"),
str_to_title(names(b5_vars)))),
trait2 = fct_reorder(trait2, sqdiff, .desc = T))
plot_sqdiff_cg <- ggplot(sqdiff_df_plot_cg) +
aes(x = as.factor(time), y = sqdiff) +
geom_boxplot() +
geom_violin(fill = NA) +
ggforce::facet_wrap_paginate(~trait2, ncol=3, nrow=2) +
labs(x = "Measurement Occasion", y = "Squared difference") +
theme_bw()
plot_sqdiff_cg + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 1)
plot_sqdiff_cg + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 2)
plot_sqdiff_cg + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 3)
plot_sqdiff_cg + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 4)
plot_sqdiff_cg + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 5)
Traits/facets sorted by average size of squared differences - in the intervention groups from T2 to T3
sqdiff_df_plot_fu <- df_sbsa2 %>%
filter(rando!="Waitlist-Control" & time %in% c(2,3)) %>%
select(pid, time, all_of(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"))) %>%
pivot_longer(-c(pid, time), names_to = "trait", values_to = "sqdiff") %>%
group_by(trait) %>%
mutate(cur_group_id()) %>%
ungroup() %>%
filter(!is.na(sqdiff)) %>%
mutate(trait2 = fct_recode(trait, !!! setNames(paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_sqdiff"),
str_to_title(names(b5_vars)))),
trait2 = fct_reorder(trait2, sqdiff, .desc = T))
plot_sqdiff_fu <- ggplot(sqdiff_df_plot_fu) +
aes(x = as.factor(time), y = sqdiff) +
geom_boxplot() +
geom_violin(fill = NA) +
ggforce::facet_wrap_paginate(~trait2, ncol=3, nrow=2) +
labs(x = "Measurement Occasion", y = "Squared difference") +
theme_bw()
plot_sqdiff_fu + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 1)
plot_sqdiff_fu + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 2)
plot_sqdiff_fu + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 3)
plot_sqdiff_fu + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 4)
plot_sqdiff_fu + ggforce::facet_wrap_paginate(~trait2, ncol=4, nrow=1, page = 5)
Not specifically preregistered for study 2 because we were foremost interested in the moderation hypothesis (H6 in prereg / H2 in paper) and thought that it was somewhat redundant with these models of correlations with the change score. I still ran the models for study 2 here (without the control group and for changes between T1 and T2) in case the comparison with study 1 is of interest.
In the self-improvement group, there will be a correlation between change goals and change in current-self ratings but not change in ideal-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level differences across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
Reshape and split data set by intervention group:
# from T1 to T2
df_sbsa2_wide_pers_sb <- df_sbsa2 %>%
filter(rando=="Self-Improvement" & time %in% c(1,2)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07")), # facet-specific change goals
sb06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sb07"), sb06_01)) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2, sb06_01_t2))
# colnames(df_sbsa2_wide_pers_sb)
group_assign <- df_sbsa2 %>% select(pid, rando) %>% unique()
df_sbsa2_wide_pers_sb <- df_sbsa2_wide_pers %>% left_join(group_assign) %>%
filter(rando=="Self-Improvement") %>% select(-rando, -ends_with("_t3")) %>%
left_join(df_sbsa2_wide_pers_sb)
# follow-up, from T2 to T3 (for later models)
df_sbsa2_wide_pers_sb_fu <- df_sbsa2 %>%
filter(rando=="Self-Improvement" & time %in% c(2,3)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07")), # facet-specific change goals
sb06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sb07"), sb06_01)) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2, sb06_01_t2))
# colnames(df_sbsa2_wide_pers_sb)
df_sbsa2_wide_pers_sb_fu <- df_sbsa2_wide_pers %>% left_join(group_assign) %>%
filter(rando=="Self-Improvement" & !is.na(valid_t2)) %>% select(-rando, -ends_with("_t1")) %>%
left_join(df_sbsa2_wide_pers_sb_fu)Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_hyp4 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_extra_curr_hyp4 <- lavaan(mi_lcs_extra_curr_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 4616.485 | 4685.3 | 0.938 | 0.109 | 0.107 |
# parameters of interest
params_lcs_extra_curr_hyp4 <- broom::tidy(fit_mi_lcs_extra_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.097 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_extra_1 ~1 | 0.709 | 0.437 | 0.981 | 2.436 | 5.114 | 0.000 |
| extra_t1 ~1 | 2.989 | 2.903 | 3.075 | 5.066 | 68.090 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.070 | 0.035 | 0.105 | 0.826 | 3.955 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.206 | -0.296 | -0.116 | -0.417 | -4.476 | 0.000 |
| d_extra_1 ~~ sb06_01_t1 | -0.026 | -0.073 | 0.021 | -0.097 | -1.090 | 0.276 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_hyp4 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_extra_ideal_hyp4 <- lavaan(mi_lcs_extra_ideal_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 4118.071 | 4186.886 | 0.993 | 0.025 | 0.045 |
# parameters of interest
params_lcs_extra_ideal_hyp4 <- broom::tidy(fit_mi_lcs_extra_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.976 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_extra_1 ~1 | 0.652 | 0.131 | 1.173 | 2.720 | 2.451 | 0.014 |
| extra_t1 ~1 | 3.682 | 3.615 | 3.749 | 9.893 | 107.796 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.053 | 0.023 | 0.083 | 0.920 | 3.477 | 0.001 |
| d_extra_1 ~ extra_t1 | -0.182 | -0.322 | -0.043 | -0.283 | -2.557 | 0.011 |
| d_extra_1 ~~ sb06_01_t1 | 0.002 | -0.043 | 0.047 | 0.009 | 0.094 | 0.925 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_specif_hyp4 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_01_t1 ~~ sb07_01_t1
sb07_02_t1 ~~ sb07_02_t1
sb07_03_t1 ~~ sb07_03_t1
sb07_01_t1 ~ 1
sb07_02_t1 ~ 1
sb07_03_t1 ~ 1
'
fit_mi_lcs_extra_curr_specif_hyp4 <- lavaan(mi_lcs_extra_curr_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6968.653 | 7064.229 | 0.877 | 0.124 | 0.144 |
# parameters of interest
params_lcs_extra_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.120 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.570 | NA | NA |
| d_extra_1 ~1 | 0.782 | 0.482 | 1.082 | 2.626 | 5.110 | 0.000 |
| extra_t1 ~1 | 2.990 | 2.904 | 3.076 | 5.110 | 68.086 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.070 | 0.036 | 0.105 | 0.794 | 4.021 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.231 | -0.330 | -0.131 | -0.454 | -4.552 | 0.000 |
| goals ~~ d_extra_1 | -0.046 | -0.117 | 0.025 | -0.263 | -1.264 | 0.206 |
| goals ~~ goals | 0.431 | 0.010 | 0.853 | 1.000 | 2.008 | 0.045 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_specif_hyp4 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_01_t1 ~~ sb07_01_t1
sb07_02_t1 ~~ sb07_02_t1
sb07_03_t1 ~~ sb07_03_t1
sb07_01_t1 ~ 1
sb07_02_t1 ~ 1
sb07_03_t1 ~ 1
'
fit_mi_lcs_extra_ideal_specif_hyp4 <- lavaan(mi_lcs_extra_ideal_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6472.111 | 6567.687 | 0.995 | 0.018 | 0.034 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.977 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.631 | NA | NA |
| d_extra_1 ~1 | 0.661 | 0.149 | 1.173 | 2.757 | 2.530 | 0.011 |
| extra_t1 ~1 | 3.682 | 3.615 | 3.749 | 9.909 | 107.814 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.053 | 0.023 | 0.082 | 0.918 | 3.495 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.185 | -0.322 | -0.048 | -0.286 | -2.640 | 0.008 |
| goals ~~ d_extra_1 | 0.016 | -0.022 | 0.053 | 0.118 | 0.812 | 0.417 |
| goals ~~ goals | 0.329 | -0.001 | 0.659 | 1.000 | 1.953 | 0.051 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_hyp4 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_agree_curr_hyp4 <- lavaan(mi_lcs_agree_curr_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 3951.479 | 4020.293 | 0.999 | 0.011 | 0.05 |
# parameters of interest
params_lcs_agree_curr_hyp4 <- broom::tidy(fit_mi_lcs_agree_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.530 | NA | NA |
| d_agree_1 ~1 | 0.664 | 0.296 | 1.031 | 3.282 | 3.541 | 0.000 |
| agree_t1 ~1 | 3.463 | 3.395 | 3.531 | 8.480 | 99.783 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.035 | 0.015 | 0.056 | 0.859 | 3.363 | 0.001 |
| d_agree_1 ~ agree_t1 | -0.186 | -0.292 | -0.080 | -0.376 | -3.453 | 0.001 |
| d_agree_1 ~~ sb06_01_t1 | 0.023 | -0.009 | 0.054 | 0.118 | 1.383 | 0.167 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_hyp4 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_agree_ideal_hyp4 <- lavaan(mi_lcs_agree_ideal_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 3955.753 | 4024.568 | 0.992 | 0.031 | 0.029 |
# parameters of interest
params_lcs_agree_ideal_hyp4 <- broom::tidy(fit_mi_lcs_agree_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.054 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.646 | NA | NA |
| d_agree_1 ~1 | 0.866 | 0.440 | 1.292 | 3.192 | 3.985 | 0.000 |
| agree_t1 ~1 | 3.769 | 3.700 | 3.839 | 8.516 | 106.115 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.063 | 0.036 | 0.090 | 0.850 | 4.544 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.238 | -0.349 | -0.127 | -0.388 | -4.213 | 0.000 |
| d_agree_1 ~~ sb06_01_t1 | 0.009 | -0.030 | 0.048 | 0.034 | 0.438 | 0.662 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_specif_hyp4 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_04_t1 ~~ sb07_04_t1
sb07_05_t1 ~~ sb07_05_t1
sb07_06_t1 ~~ sb07_06_t1
sb07_04_t1 ~ 1
sb07_05_t1 ~ 1
sb07_06_t1 ~ 1
'
fit_mi_lcs_agree_curr_specif_hyp4 <- lavaan(mi_lcs_agree_curr_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6266.535 | 6362.111 | 0.976 | 0.048 | 0.082 |
# parameters of interest
params_lcs_agree_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.083 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.549 | NA | NA |
| d_agree_1 ~1 | 0.720 | 0.343 | 1.097 | 3.472 | 3.741 | 0.000 |
| agree_t1 ~1 | 3.463 | 3.395 | 3.531 | 8.462 | 99.734 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.036 | 0.016 | 0.057 | 0.841 | 3.474 | 0.001 |
| d_agree_1 ~ agree_t1 | -0.202 | -0.311 | -0.093 | -0.399 | -3.648 | 0.000 |
| goals ~~ d_agree_1 | -0.014 | -0.051 | 0.023 | -0.078 | -0.728 | 0.467 |
| goals ~~ goals | 0.861 | 0.566 | 1.156 | 1.000 | 5.722 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_specif_hyp4 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_04_t1 ~~ sb07_04_t1
sb07_05_t1 ~~ sb07_05_t1
sb07_06_t1 ~~ sb07_06_t1
sb07_04_t1 ~ 1
sb07_05_t1 ~ 1
sb07_06_t1 ~ 1
'
fit_mi_lcs_agree_ideal_specif_hyp4 <- lavaan(mi_lcs_agree_ideal_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6269.691 | 6365.267 | 0.977 | 0.048 | 0.058 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.055 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.647 | NA | NA |
| d_agree_1 ~1 | 0.869 | 0.441 | 1.298 | 3.201 | 3.975 | 0.00 |
| agree_t1 ~1 | 3.769 | 3.700 | 3.839 | 8.518 | 106.129 | 0.00 |
| d_agree_1 ~~ d_agree_1 | 0.063 | 0.036 | 0.090 | 0.849 | 4.533 | 0.00 |
| d_agree_1 ~ agree_t1 | -0.239 | -0.350 | -0.127 | -0.389 | -4.197 | 0.00 |
| goals ~~ d_agree_1 | -0.004 | -0.051 | 0.044 | -0.016 | -0.150 | 0.88 |
| goals ~~ goals | 0.866 | 0.570 | 1.162 | 1.000 | 5.738 | 0.00 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_hyp4 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_consc_curr_hyp4 <- lavaan(mi_lcs_consc_curr_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 4212.613 | 4281.428 | 0.993 | 0.043 | 0.059 |
# parameters of interest
params_lcs_consc_curr_hyp4 <- broom::tidy(fit_mi_lcs_consc_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.529 | NA | NA |
| d_consc_1 ~1 | 0.553 | 0.220 | 0.885 | 1.349 | 3.260 | 0.001 |
| consc_t1 ~1 | 3.378 | 3.287 | 3.469 | 4.396 | 72.808 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.157 | 0.081 | 0.234 | 0.938 | 4.015 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.133 | -0.231 | -0.036 | -0.250 | -2.673 | 0.008 |
| d_consc_1 ~~ sb06_01_t1 | 0.022 | -0.048 | 0.092 | 0.055 | 0.619 | 0.536 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_hyp4 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_consc_ideal_hyp4 <- lavaan(mi_lcs_consc_ideal_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 3574.92 | 3643.735 | 0.989 | 0.038 | 0.043 |
# parameters of interest
params_lcs_consc_ideal_hyp4 <- broom::tidy(fit_mi_lcs_consc_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.937 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.625 | NA | NA |
| d_consc_1 ~1 | 0.668 | -0.029 | 1.365 | 2.520 | 1.878 | 0.060 |
| consc_t1 ~1 | 4.319 | 4.257 | 4.382 | 10.868 | 135.542 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.067 | 0.039 | 0.094 | 0.948 | 4.700 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.153 | -0.310 | 0.005 | -0.229 | -1.903 | 0.057 |
| d_consc_1 ~~ sb06_01_t1 | 0.011 | -0.030 | 0.053 | 0.044 | 0.538 | 0.590 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_specif_hyp4 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_07_t1 ~~ sb07_07_t1
sb07_08_t1 ~~ sb07_08_t1
sb07_09_t1 ~~ sb07_09_t1
sb07_07_t1 ~ 1
sb07_08_t1 ~ 1
sb07_09_t1 ~ 1
'
fit_mi_lcs_consc_curr_specif_hyp4 <- lavaan(mi_lcs_consc_curr_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6503.589 | 6599.165 | 0.918 | 0.133 | 0.239 |
# parameters of interest
params_lcs_consc_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.052 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.596 | NA | NA |
| d_consc_1 ~1 | 0.810 | 0.422 | 1.199 | 1.859 | 4.086 | 0.000 |
| consc_t1 ~1 | 3.378 | 3.287 | 3.469 | 4.392 | 72.805 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.164 | 0.085 | 0.243 | 0.864 | 4.078 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.209 | -0.324 | -0.093 | -0.368 | -3.545 | 0.000 |
| goals ~~ d_consc_1 | -0.108 | -0.197 | -0.018 | -0.230 | -2.365 | 0.018 |
| goals ~~ goals | 1.341 | 1.034 | 1.648 | 1.000 | 8.555 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_specif_hyp4 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_07_t1 ~~ sb07_07_t1
sb07_08_t1 ~~ sb07_08_t1
sb07_09_t1 ~~ sb07_09_t1
sb07_07_t1 ~ 1
sb07_08_t1 ~ 1
sb07_09_t1 ~ 1
'
fit_mi_lcs_consc_ideal_specif_hyp4 <- lavaan(mi_lcs_consc_ideal_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 5869.922 | 5965.498 | 0.999 | 0.008 | 0.031 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.934 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.620 | NA | NA |
| d_consc_1 ~1 | 0.645 | -0.052 | 1.343 | 2.435 | 1.813 | 0.070 |
| consc_t1 ~1 | 4.319 | 4.257 | 4.382 | 10.815 | 135.500 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.067 | 0.039 | 0.095 | 0.951 | 4.695 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.147 | -0.304 | 0.010 | -0.222 | -1.837 | 0.066 |
| goals ~~ d_consc_1 | -0.035 | -0.089 | 0.019 | -0.116 | -1.255 | 0.209 |
| goals ~~ goals | 1.336 | 1.027 | 1.646 | 1.000 | 8.452 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_hyp4 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_neuro_curr_hyp4 <- lavaan(mi_lcs_neuro_curr_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 4449.298 | 4518.113 | 0.969 | 0.094 | 0.13 |
# parameters of interest
params_lcs_neuro_curr_hyp4 <- broom::tidy(fit_mi_lcs_neuro_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.069 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.586 | NA | NA |
| d_neuro_1 ~1 | 0.516 | 0.241 | 0.792 | 1.122 | 3.673 | 0.000 |
| neuro_t1 ~1 | 3.276 | 3.179 | 3.372 | 3.898 | 66.630 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.180 | 0.135 | 0.224 | 0.849 | 7.926 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.213 | -0.298 | -0.127 | -0.388 | -4.891 | 0.000 |
| d_neuro_1 ~~ sb06_01_t1 | -0.004 | -0.073 | 0.064 | -0.010 | -0.123 | 0.902 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_hyp4 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_hyp4 <- lavaan(mi_lcs_neuro_ideal_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 3587.547 | 3656.362 | 1 | 0.007 | 0.039 |
# parameters of interest
params_lcs_neuro_ideal_hyp4 <- broom::tidy(fit_mi_lcs_neuro_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.949 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.765 | NA | NA |
| d_neuro_1 ~1 | 0.502 | 0.201 | 0.804 | 1.368 | 3.266 | 0.001 |
| neuro_t1 ~1 | 1.707 | 1.648 | 1.766 | 3.747 | 56.782 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.120 | 0.078 | 0.162 | 0.888 | 5.572 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.270 | -0.455 | -0.084 | -0.334 | -2.852 | 0.004 |
| d_neuro_1 ~~ sb06_01_t1 | -0.046 | -0.099 | 0.008 | -0.130 | -1.665 | 0.096 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_specif_hyp4 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_10_t1 ~~ sb07_10_t1
sb07_11_t1 ~~ sb07_11_t1
sb07_12_t1 ~~ sb07_12_t1
sb07_10_t1 ~ 1
sb07_11_t1 ~ 1
sb07_12_t1 ~ 1
'
fit_mi_lcs_neuro_curr_specif_hyp4 <- lavaan(mi_lcs_neuro_curr_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6888.104 | 6983.681 | 0.911 | 0.138 | 0.242 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.152 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.690 | NA | NA |
| d_neuro_1 ~1 | 0.813 | 0.496 | 1.129 | 1.616 | 5.031 | 0.000 |
| neuro_t1 ~1 | 3.276 | 3.179 | 3.372 | 3.903 | 66.640 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.188 | 0.141 | 0.236 | 0.745 | 7.721 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.303 | -0.400 | -0.205 | -0.505 | -6.106 | 0.000 |
| goals ~~ d_neuro_1 | 0.135 | 0.048 | 0.222 | 0.287 | 3.053 | 0.002 |
| goals ~~ goals | 1.176 | 0.842 | 1.511 | 1.000 | 6.896 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_specif_hyp4 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_10_t1 ~~ sb07_10_t1
sb07_11_t1 ~~ sb07_11_t1
sb07_12_t1 ~~ sb07_12_t1
sb07_10_t1 ~ 1
sb07_11_t1 ~ 1
sb07_12_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_specif_hyp4 <- lavaan(mi_lcs_neuro_ideal_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6038.494 | 6134.07 | 0.995 | 0.023 | 0.048 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.939 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.749 | NA | NA |
| d_neuro_1 ~1 | 0.470 | 0.174 | 0.767 | 1.294 | 3.108 | 0.002 |
| neuro_t1 ~1 | 1.707 | 1.648 | 1.766 | 3.746 | 56.787 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.119 | 0.077 | 0.161 | 0.901 | 5.567 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.251 | -0.433 | -0.068 | -0.314 | -2.691 | 0.007 |
| goals ~~ d_neuro_1 | 0.027 | -0.036 | 0.090 | 0.071 | 0.847 | 0.397 |
| goals ~~ goals | 1.227 | 0.894 | 1.559 | 1.000 | 7.233 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_hyp4 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_openn_curr_hyp4 <- lavaan(mi_lcs_openn_curr_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 4166.092 | 4234.907 | 0.991 | 0.04 | 0.039 |
# parameters of interest
params_lcs_openn_curr_hyp4 <- broom::tidy(fit_mi_lcs_openn_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.999 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.457 | NA | NA |
| d_openn_1 ~1 | 0.438 | 0.105 | 0.770 | 1.647 | 2.577 | 0.010 |
| openn_t1 ~1 | 3.485 | 3.410 | 3.561 | 6.002 | 90.551 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.067 | 0.037 | 0.097 | 0.949 | 4.382 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.103 | -0.196 | -0.011 | -0.226 | -2.182 | 0.029 |
| d_openn_1 ~~ sb06_01_t1 | -0.016 | -0.061 | 0.029 | -0.060 | -0.692 | 0.489 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with manifest change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_hyp4 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb06_01_t1 ~~ sb06_01_t1
sb06_01_t1 ~ 1
'
fit_mi_lcs_openn_ideal_hyp4 <- lavaan(mi_lcs_openn_ideal_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 18 | 4025.404 | 4094.219 | 0.985 | 0.042 | 0.039 |
# parameters of interest
params_lcs_openn_ideal_hyp4 <- broom::tidy(fit_mi_lcs_openn_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.517 | NA | NA |
| d_openn_1 ~1 | 0.614 | 0.125 | 1.102 | 4.142 | 2.461 | 0.014 |
| openn_t1 ~1 | 3.917 | 3.856 | 3.979 | 13.318 | 125.051 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.020 | 0.007 | 0.033 | 0.908 | 3.081 | 0.002 |
| d_openn_1 ~ openn_t1 | -0.153 | -0.274 | -0.031 | -0.303 | -2.457 | 0.014 |
| d_openn_1 ~~ sb06_01_t1 | 0.003 | -0.023 | 0.028 | 0.018 | 0.197 | 0.844 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_specif_hyp4 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_13_t1 ~~ sb07_13_t1
sb07_14_t1 ~~ sb07_14_t1
sb07_15_t1 ~~ sb07_15_t1
sb07_13_t1 ~ 1
sb07_14_t1 ~ 1
sb07_15_t1 ~ 1
'
fit_mi_lcs_openn_curr_specif_hyp4 <- lavaan(mi_lcs_openn_curr_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6309.866 | 6405.443 | 0.965 | 0.073 | 0.116 |
# parameters of interest
params_lcs_openn_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.009 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.468 | NA | NA |
| d_openn_1 ~1 | 0.486 | 0.139 | 0.833 | 1.806 | 2.743 | 0.006 |
| openn_t1 ~1 | 3.485 | 3.410 | 3.561 | 6.009 | 90.613 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.068 | 0.038 | 0.098 | 0.936 | 4.438 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.117 | -0.214 | -0.020 | -0.252 | -2.364 | 0.018 |
| goals ~~ d_openn_1 | -0.028 | -0.067 | 0.011 | -0.134 | -1.392 | 0.164 |
| goals ~~ goals | 0.628 | 0.416 | 0.840 | 1.000 | 5.801 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_specif_hyp4 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) change goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb07_13_t1 ~~ sb07_13_t1
sb07_14_t1 ~~ sb07_14_t1
sb07_15_t1 ~~ sb07_15_t1
sb07_13_t1 ~ 1
sb07_14_t1 ~ 1
sb07_15_t1 ~ 1
'
fit_mi_lcs_openn_ideal_specif_hyp4 <- lavaan(mi_lcs_openn_ideal_specif_hyp4, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 25 | 6170.616 | 6266.192 | 0.977 | 0.051 | 0.06 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.517 | NA | NA |
| d_openn_1 ~1 | 0.614 | 0.128 | 1.100 | 4.147 | 2.474 | 0.013 |
| openn_t1 ~1 | 3.917 | 3.856 | 3.979 | 13.331 | 125.069 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.020 | 0.007 | 0.032 | 0.908 | 3.088 | 0.002 |
| d_openn_1 ~ openn_t1 | -0.153 | -0.274 | -0.031 | -0.303 | -2.468 | 0.014 |
| goals ~~ d_openn_1 | -0.002 | -0.025 | 0.021 | -0.018 | -0.177 | 0.860 |
| goals ~~ goals | 0.629 | 0.417 | 0.842 | 1.000 | 5.810 | 0.000 |
Run models for all facets with a template & loop:
# create template:
facet_template <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
d_facet_1 ~ facet_t1 # This estimates the self-feedback parameter
d_facet_1 ~~ ind_goal # estimates the covariance/correlation with change goal variable
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# loop across 2 BFI versions (combined pre&post current/ideal)
for (j in 5:length(bfi_versions)) {
items = paste0(bfi_versions[[j]], item_nrs)
# loop across 2 different goal operationalizations (sb06_01_t1 & sb07_XX_t1)
for (k in 1:2) {
if (k==1) {
goal_op = "sb06_01_t1"
} else{
goal_op = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
}
template_filled <- str_replace_all(facet_template,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = goal_op))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')
# save to environment
if (k==1) {
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp4")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp4")), facet_model_fit))
} else{
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), facet_model_fit))
}
}
}
} Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7959.374 | 8043.481 | 0.912 | 0.1 | 0.094 |
# parameters of interest
params_lcs_socia_curr_hyp4 <- broom::tidy(fit_mi_lcs_socia_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.091 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.515 | NA | NA |
| d_socia_1 ~1 | 0.624 | 0.368 | 0.881 | 1.616 | 4.770 | 0.000 |
| socia_t1 ~1 | 2.875 | 2.747 | 3.003 | 3.514 | 44.013 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.125 | 0.063 | 0.186 | 0.836 | 3.980 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.191 | -0.283 | -0.100 | -0.405 | -4.113 | 0.000 |
| d_socia_1 ~~ sb06_01_t1 | -0.026 | -0.088 | 0.037 | -0.071 | -0.796 | 0.426 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7791.993 | 7876.1 | 0.935 | 0.053 | 0.056 |
# parameters of interest
params_lcs_socia_ideal_hyp4 <- broom::tidy(fit_mi_lcs_socia_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.650 | NA | NA |
| d_socia_1 ~1 | 0.846 | 0.070 | 1.621 | 3.113 | 2.138 | 0.033 |
| socia_t1 ~1 | 4.241 | 4.158 | 4.324 | 10.208 | 100.432 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.066 | 0.013 | 0.120 | 0.900 | 2.441 | 0.015 |
| d_socia_1 ~ socia_t1 | -0.207 | -0.387 | -0.027 | -0.317 | -2.249 | 0.025 |
| d_socia_1 ~~ sb06_01_t1 | 0.042 | -0.012 | 0.096 | 0.162 | 1.531 | 0.126 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8102.304 | 8186.411 | 0.885 | 0.117 | 0.13 |
# parameters of interest
params_lcs_socia_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.127 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_socia_1 ~1 | 0.736 | 0.450 | 1.022 | 1.813 | 5.048 | 0.000 |
| socia_t1 ~1 | 2.876 | 2.748 | 3.004 | 3.526 | 44.021 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.130 | 0.066 | 0.193 | 0.786 | 3.989 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.230 | -0.330 | -0.130 | -0.462 | -4.506 | 0.000 |
| d_socia_1 ~~ sb07_01_t1 | -0.104 | -0.200 | -0.008 | -0.229 | -2.128 | 0.033 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.597 | 1.400 | 1.794 | 1.000 | 15.899 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7941.183 | 8025.29 | 0.947 | 0.047 | 0.056 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.613 | NA | NA |
| d_socia_1 ~1 | 0.696 | -0.062 | 1.454 | 2.628 | 1.799 | 0.072 |
| socia_t1 ~1 | 4.241 | 4.158 | 4.323 | 10.068 | 100.472 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.065 | 0.011 | 0.119 | 0.926 | 2.368 | 0.018 |
| d_socia_1 ~ socia_t1 | -0.171 | -0.347 | 0.005 | -0.273 | -1.908 | 0.056 |
| d_socia_1 ~~ sb07_01_t1 | -0.042 | -0.110 | 0.025 | -0.131 | -1.224 | 0.221 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.597 | 1.400 | 1.794 | 1.000 | 15.899 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8179.331 | 8263.438 | 0.959 | 0.061 | 0.064 |
# parameters of interest
params_lcs_asser_curr_hyp4 <- broom::tidy(fit_mi_lcs_asser_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.492 | NA | NA |
| d_asser_1 ~1 | 0.511 | 0.221 | 0.802 | 1.865 | 3.454 | 0.001 |
| asser_t1 ~1 | 3.093 | 2.973 | 3.213 | 5.362 | 50.389 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.068 | 0.016 | 0.120 | 0.906 | 2.550 | 0.011 |
| d_asser_1 ~ asser_t1 | -0.146 | -0.239 | -0.052 | -0.307 | -3.051 | 0.002 |
| d_asser_1 ~~ sb06_01_t1 | -0.019 | -0.064 | 0.025 | -0.073 | -0.848 | 0.397 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7876.725 | 7960.832 | 0.947 | 0.047 | 0.048 |
# parameters of interest
params_lcs_asser_ideal_hyp4 <- broom::tidy(fit_mi_lcs_asser_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.971 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_asser_1 ~1 | 0.723 | -0.143 | 1.588 | 3.648 | 1.636 | 0.102 |
| asser_t1 ~1 | 4.145 | 4.064 | 4.226 | 13.421 | 100.252 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.036 | -0.006 | 0.079 | 0.925 | 1.682 | 0.093 |
| d_asser_1 ~ asser_t1 | -0.175 | -0.382 | 0.031 | -0.274 | -1.663 | 0.096 |
| d_asser_1 ~~ sb06_01_t1 | -0.014 | -0.056 | 0.029 | -0.070 | -0.629 | 0.530 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8341.572 | 8425.679 | 0.925 | 0.084 | 0.088 |
# parameters of interest
params_lcs_asser_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.046 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.502 | NA | NA |
| d_asser_1 ~1 | 0.550 | 0.260 | 0.839 | 1.985 | 3.721 | 0.000 |
| asser_t1 ~1 | 3.093 | 2.973 | 3.213 | 5.363 | 50.407 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.068 | 0.016 | 0.121 | 0.891 | 2.558 | 0.011 |
| d_asser_1 ~ asser_t1 | -0.158 | -0.252 | -0.065 | -0.330 | -3.330 | 0.001 |
| d_asser_1 ~~ sb07_02_t1 | -0.068 | -0.130 | -0.006 | -0.200 | -2.134 | 0.033 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.688 | 1.496 | 1.881 | 1.000 | 17.182 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8043.873 | 8127.98 | 0.948 | 0.046 | 0.048 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.630 | NA | NA |
| d_asser_1 ~1 | 0.752 | -0.134 | 1.638 | 3.819 | 1.664 | 0.096 |
| asser_t1 ~1 | 4.145 | 4.064 | 4.226 | 13.610 | 100.283 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.036 | -0.006 | 0.077 | 0.920 | 1.691 | 0.091 |
| d_asser_1 ~ asser_t1 | -0.183 | -0.395 | 0.029 | -0.282 | -1.688 | 0.091 |
| d_asser_1 ~~ sb07_02_t1 | 0.010 | -0.038 | 0.058 | 0.041 | 0.405 | 0.686 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.688 | 1.496 | 1.881 | 1.000 | 17.182 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8355.963 | 8440.07 | 0.958 | 0.055 | 0.071 |
# parameters of interest
params_lcs_energ_curr_hyp4 <- broom::tidy(fit_mi_lcs_energ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.087 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.502 | NA | NA |
| d_energ_1 ~1 | 0.453 | 0.152 | 0.755 | 2.944 | 2.946 | 0.003 |
| energ_t1 ~1 | 2.770 | 2.662 | 2.878 | 8.314 | 50.244 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.020 | -0.001 | 0.041 | 0.842 | 1.864 | 0.062 |
| d_energ_1 ~ energ_t1 | -0.184 | -0.292 | -0.075 | -0.398 | -3.311 | 0.001 |
| d_energ_1 ~~ sb06_01_t1 | 0.016 | -0.016 | 0.048 | 0.112 | 0.976 | 0.329 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7752.633 | 7836.74 | 0.93 | 0.045 | 0.05 |
# parameters of interest
params_lcs_energ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_energ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.804 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_energ_1 ~1 | -0.020 | -0.706 | 0.667 | -0.160 | -0.056 | 0.955 |
| energ_t1 ~1 | 2.242 | 2.124 | 2.360 | 12.835 | 37.328 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.015 | -0.007 | 0.037 | 0.999 | 1.340 | 0.180 |
| d_energ_1 ~ energ_t1 | 0.026 | -0.286 | 0.339 | 0.037 | 0.164 | 0.869 |
| d_energ_1 ~~ sb06_01_t1 | 0.000 | -0.030 | 0.031 | 0.001 | 0.008 | 0.993 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8500.009 | 8584.116 | 0.89 | 0.091 | 0.104 |
# parameters of interest
params_lcs_energ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.076 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.485 | NA | NA |
| d_energ_1 ~1 | 0.414 | 0.097 | 0.731 | 2.749 | 2.561 | 0.010 |
| energ_t1 ~1 | 2.770 | 2.662 | 2.878 | 8.288 | 50.228 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.019 | -0.001 | 0.040 | 0.859 | 1.848 | 0.065 |
| d_energ_1 ~ energ_t1 | -0.170 | -0.283 | -0.057 | -0.376 | -2.943 | 0.003 |
| d_energ_1 ~~ sb07_03_t1 | -0.001 | -0.037 | 0.034 | -0.008 | -0.074 | 0.941 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.572 | 1.373 | 1.770 | 1.000 | 15.550 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7895.287 | 7979.394 | 0.939 | 0.042 | 0.05 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.804 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.564 | NA | NA |
| d_energ_1 ~1 | -0.024 | -0.720 | 0.672 | -0.196 | -0.068 | 0.946 |
| energ_t1 ~1 | 2.242 | 2.124 | 2.360 | 12.830 | 37.323 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.015 | -0.007 | 0.037 | 0.998 | 1.338 | 0.181 |
| d_energ_1 ~ energ_t1 | 0.028 | -0.289 | 0.345 | 0.040 | 0.175 | 0.861 |
| d_energ_1 ~~ sb07_03_t1 | 0.003 | -0.030 | 0.035 | 0.016 | 0.153 | 0.879 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.572 | 1.373 | 1.770 | 1.000 | 15.550 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8081.935 | 8166.042 | 0.965 | 0.044 | 0.049 |
# parameters of interest
params_lcs_compa_curr_hyp4 <- broom::tidy(fit_mi_lcs_compa_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.127 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.455 | NA | NA |
| d_compa_1 ~1 | 0.886 | 0.412 | 1.360 | 3.640 | 3.661 | 0.000 |
| compa_t1 ~1 | 4.184 | 4.090 | 4.277 | 6.940 | 87.951 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.046 | 0.001 | 0.092 | 0.784 | 2.004 | 0.045 |
| d_compa_1 ~ compa_t1 | -0.188 | -0.297 | -0.079 | -0.465 | -3.380 | 0.001 |
| d_compa_1 ~~ sb06_01_t1 | 0.027 | -0.025 | 0.079 | 0.123 | 1.014 | 0.311 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8200.707 | 8284.814 | 0.983 | 0.024 | 0.048 |
# parameters of interest
params_lcs_compa_ideal_hyp4 <- broom::tidy(fit_mi_lcs_compa_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.427 | NA | NA |
| d_compa_1 ~1 | 0.269 | -0.757 | 1.295 | 1.204 | 0.514 | 0.607 |
| compa_t1 ~1 | 4.225 | 4.132 | 4.319 | 8.252 | 88.493 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.049 | -0.027 | 0.124 | 0.972 | 1.262 | 0.207 |
| d_compa_1 ~ compa_t1 | -0.073 | -0.310 | 0.163 | -0.168 | -0.608 | 0.543 |
| d_compa_1 ~~ sb06_01_t1 | -0.030 | -0.087 | 0.028 | -0.133 | -1.012 | 0.312 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8279.231 | 8363.338 | 0.925 | 0.067 | 0.079 |
# parameters of interest
params_lcs_compa_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.136 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.468 | NA | NA |
| d_compa_1 ~1 | 0.926 | 0.444 | 1.407 | 3.762 | 3.769 | 0.000 |
| compa_t1 ~1 | 4.184 | 4.091 | 4.277 | 7.011 | 88.158 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.047 | 0.003 | 0.091 | 0.770 | 2.074 | 0.038 |
| d_compa_1 ~ compa_t1 | -0.198 | -0.309 | -0.086 | -0.479 | -3.473 | 0.001 |
| d_compa_1 ~~ sb07_04_t1 | -0.010 | -0.087 | 0.066 | -0.035 | -0.264 | 0.792 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.843 | 1.662 | 2.024 | 1.000 | 19.987 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8397.825 | 8481.932 | 0.914 | 0.055 | 0.067 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.976 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.423 | NA | NA |
| d_compa_1 ~1 | 0.253 | -0.769 | 1.274 | 1.132 | 0.485 | 0.628 |
| compa_t1 ~1 | 4.225 | 4.132 | 4.319 | 8.211 | 88.429 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.049 | -0.028 | 0.125 | 0.974 | 1.245 | 0.213 |
| d_compa_1 ~ compa_t1 | -0.070 | -0.305 | 0.166 | -0.160 | -0.579 | 0.563 |
| d_compa_1 ~~ sb07_04_t1 | 0.018 | -0.070 | 0.107 | 0.061 | 0.403 | 0.687 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.843 | 1.662 | 2.024 | 1.000 | 19.987 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7006.72 | 7090.827 | 0.949 | 0.063 | 0.066 |
# parameters of interest
params_lcs_respe_curr_hyp4 <- broom::tidy(fit_mi_lcs_respe_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.165 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.638 | NA | NA |
| d_respe_1 ~1 | 1.277 | 0.632 | 1.923 | 4.128 | 3.880 | 0.000 |
| respe_t1 ~1 | 4.474 | 4.396 | 4.552 | 7.919 | 111.900 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.070 | 0.030 | 0.111 | 0.736 | 3.421 | 0.001 |
| d_respe_1 ~ respe_t1 | -0.282 | -0.422 | -0.141 | -0.514 | -3.930 | 0.000 |
| d_respe_1 ~~ sb06_01_t1 | 0.000 | -0.051 | 0.052 | 0.001 | 0.013 | 0.990 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 6614.559 | 6698.666 | 0.964 | 0.042 | 0.046 |
# parameters of interest
params_lcs_respe_ideal_hyp4 <- broom::tidy(fit_mi_lcs_respe_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.105 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.674 | NA | NA |
| d_respe_1 ~1 | 1.301 | 0.526 | 2.077 | 5.450 | 3.288 | 0.001 |
| respe_t1 ~1 | 4.710 | 4.650 | 4.770 | 12.020 | 153.576 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.045 | 0.015 | 0.076 | 0.794 | 2.938 | 0.003 |
| d_respe_1 ~ respe_t1 | -0.277 | -0.437 | -0.116 | -0.454 | -3.384 | 0.001 |
| d_respe_1 ~~ sb06_01_t1 | 0.009 | -0.037 | 0.055 | 0.040 | 0.367 | 0.714 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7196.438 | 7280.545 | 0.905 | 0.088 | 0.102 |
# parameters of interest
params_lcs_respe_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.198 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.684 | NA | NA |
| d_respe_1 ~1 | 1.428 | 0.766 | 2.090 | 4.419 | 4.229 | 0.000 |
| respe_t1 ~1 | 4.474 | 4.396 | 4.552 | 7.905 | 111.873 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.073 | 0.032 | 0.113 | 0.696 | 3.543 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.315 | -0.459 | -0.170 | -0.551 | -4.272 | 0.000 |
| d_respe_1 ~~ sb07_05_t1 | -0.066 | -0.133 | 0.002 | -0.180 | -1.915 | 0.056 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.826 | 1.607 | 2.046 | 1.000 | 16.296 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 6805.018 | 6889.125 | 0.961 | 0.044 | 0.052 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.116 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.695 | NA | NA |
| d_respe_1 ~1 | 1.376 | 0.612 | 2.140 | 5.662 | 3.531 | 0.000 |
| respe_t1 ~1 | 4.710 | 4.650 | 4.770 | 12.065 | 153.595 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.046 | 0.016 | 0.076 | 0.780 | 3.017 | 0.003 |
| d_respe_1 ~ respe_t1 | -0.292 | -0.450 | -0.134 | -0.469 | -3.625 | 0.000 |
| d_respe_1 ~~ sb07_05_t1 | -0.050 | -0.111 | 0.011 | -0.171 | -1.591 | 0.112 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.826 | 1.607 | 2.046 | 1.000 | 16.296 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8342.903 | 8427.01 | 0.927 | 0.071 | 0.078 |
# parameters of interest
params_lcs_trust_curr_hyp4 <- broom::tidy(fit_mi_lcs_trust_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.065 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.490 | NA | NA |
| d_trust_1 ~1 | 0.372 | 0.040 | 0.705 | 1.265 | 2.196 | 0.028 |
| trust_t1 ~1 | 2.836 | 2.712 | 2.960 | 4.434 | 44.798 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.076 | 0.010 | 0.142 | 0.872 | 2.244 | 0.025 |
| d_trust_1 ~ trust_t1 | -0.165 | -0.282 | -0.047 | -0.358 | -2.747 | 0.006 |
| d_trust_1 ~~ sb06_01_t1 | -0.035 | -0.099 | 0.028 | -0.127 | -1.089 | 0.276 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8016.542 | 8100.649 | 0.958 | 0.043 | 0.047 |
# parameters of interest
params_lcs_trust_ideal_hyp4 <- broom::tidy(fit_mi_lcs_trust_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.897 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.338 | NA | NA |
| d_trust_1 ~1 | -0.033 | -0.404 | 0.338 | -0.254 | -0.176 | 0.860 |
| trust_t1 ~1 | 1.900 | 1.808 | 1.993 | 5.456 | 40.189 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.017 | -0.020 | 0.054 | 0.982 | 0.909 | 0.364 |
| d_trust_1 ~ trust_t1 | 0.050 | -0.145 | 0.245 | 0.133 | 0.504 | 0.614 |
| d_trust_1 ~~ sb06_01_t1 | -0.006 | -0.051 | 0.039 | -0.045 | -0.260 | 0.795 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8537.293 | 8621.4 | 0.941 | 0.063 | 0.065 |
# parameters of interest
params_lcs_trust_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.069 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.500 | NA | NA |
| d_trust_1 ~1 | 0.392 | 0.068 | 0.716 | 1.305 | 2.370 | 0.018 |
| trust_t1 ~1 | 2.836 | 2.712 | 2.960 | 4.418 | 44.795 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.078 | 0.012 | 0.144 | 0.865 | 2.320 | 0.020 |
| d_trust_1 ~ trust_t1 | -0.172 | -0.285 | -0.059 | -0.368 | -2.975 | 0.003 |
| d_trust_1 ~~ sb07_06_t1 | -0.033 | -0.122 | 0.055 | -0.088 | -0.739 | 0.460 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.829 | 1.657 | 2.000 | 1.000 | 20.861 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8209.045 | 8293.152 | 0.959 | 0.043 | 0.047 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.897 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.336 | NA | NA |
| d_trust_1 ~1 | -0.036 | -0.406 | 0.334 | -0.273 | -0.189 | 0.850 |
| trust_t1 ~1 | 1.900 | 1.808 | 1.993 | 5.454 | 40.199 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.017 | -0.020 | 0.053 | 0.981 | 0.902 | 0.367 |
| d_trust_1 ~ trust_t1 | 0.052 | -0.143 | 0.246 | 0.138 | 0.520 | 0.603 |
| d_trust_1 ~~ sb07_06_t1 | -0.032 | -0.094 | 0.030 | -0.184 | -1.019 | 0.308 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.829 | 1.657 | 2.000 | 1.000 | 20.861 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7871.053 | 7955.16 | 0.981 | 0.047 | 0.037 |
# parameters of interest
params_lcs_organ_curr_hyp4 <- broom::tidy(fit_mi_lcs_organ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.453 | NA | NA |
| d_organ_1 ~1 | 0.235 | -0.022 | 0.493 | 0.511 | 1.794 | 0.073 |
| organ_t1 ~1 | 2.856 | 2.709 | 3.004 | 2.771 | 37.961 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.198 | 0.083 | 0.313 | 0.936 | 3.386 | 0.001 |
| d_organ_1 ~ organ_t1 | -0.113 | -0.203 | -0.023 | -0.254 | -2.469 | 0.014 |
| d_organ_1 ~~ sb06_01_t1 | -0.007 | -0.089 | 0.076 | -0.015 | -0.163 | 0.871 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7060.943 | 7145.05 | 0.797 | 0.077 | 0.089 |
# parameters of interest
params_lcs_organ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_organ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.709 | NA | NA |
| d_organ_1 ~1 | 0.384 | 0.076 | 0.691 | 1.353 | 2.446 | 0.014 |
| organ_t1 ~1 | 1.679 | 1.575 | 1.782 | 4.282 | 31.762 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.071 | 0.017 | 0.126 | 0.889 | 2.592 | 0.010 |
| d_organ_1 ~ organ_t1 | -0.241 | -0.439 | -0.043 | -0.333 | -2.385 | 0.017 |
| d_organ_1 ~~ sb06_01_t1 | 0.004 | -0.048 | 0.057 | 0.016 | 0.165 | 0.869 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8123.153 | 8207.26 | 0.857 | 0.137 | 0.168 |
# parameters of interest
params_lcs_organ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.065 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.513 | NA | NA |
| d_organ_1 ~1 | 0.411 | 0.085 | 0.736 | 0.829 | 2.473 | 0.013 |
| organ_t1 ~1 | 2.856 | 2.709 | 3.003 | 2.776 | 37.969 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.213 | 0.096 | 0.330 | 0.868 | 3.575 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.175 | -0.287 | -0.063 | -0.363 | -3.059 | 0.002 |
| d_organ_1 ~~ sb07_07_t1 | 0.140 | -0.005 | 0.285 | 0.204 | 1.890 | 0.059 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.198 | 2.006 | 2.391 | 1.000 | 22.374 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7315.24 | 7399.347 | 0.764 | 0.084 | 0.092 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.715 | NA | NA |
| d_organ_1 ~1 | 0.392 | 0.087 | 0.696 | 1.382 | 2.521 | 0.012 |
| organ_t1 ~1 | 1.678 | 1.575 | 1.782 | 4.328 | 31.814 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.071 | 0.017 | 0.126 | 0.888 | 2.571 | 0.010 |
| d_organ_1 ~ organ_t1 | -0.245 | -0.441 | -0.049 | -0.335 | -2.448 | 0.014 |
| d_organ_1 ~~ sb07_07_t1 | 0.048 | -0.035 | 0.132 | 0.122 | 1.133 | 0.257 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.198 | 2.006 | 2.391 | 1.000 | 22.374 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7909.008 | 7993.115 | 0.941 | 0.079 | 0.07 |
# parameters of interest
params_lcs_produ_curr_hyp4 <- broom::tidy(fit_mi_lcs_produ_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.021 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.655 | NA | NA |
| d_produ_1 ~1 | 0.609 | 0.191 | 1.026 | 1.011 | 2.857 | 0.004 |
| produ_t1 ~1 | 3.417 | 3.284 | 3.551 | 3.641 | 50.178 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.318 | 0.154 | 0.481 | 0.876 | 3.807 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.226 | -0.345 | -0.106 | -0.352 | -3.708 | 0.000 |
| d_produ_1 ~~ sb06_01_t1 | -0.004 | -0.108 | 0.100 | -0.007 | -0.075 | 0.940 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 5970.44 | 6054.547 | 0.915 | 0.071 | 0.064 |
# parameters of interest
params_lcs_produ_ideal_hyp4 <- broom::tidy(fit_mi_lcs_produ_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.887 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.584 | NA | NA |
| d_produ_1 ~1 | 0.141 | -0.162 | 0.443 | 0.557 | 0.913 | 0.361 |
| produ_t1 ~1 | 1.596 | 1.517 | 1.675 | 4.156 | 39.452 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.063 | 0.009 | 0.117 | 0.985 | 2.292 | 0.022 |
| d_produ_1 ~ produ_t1 | -0.081 | -0.283 | 0.122 | -0.123 | -0.782 | 0.434 |
| d_produ_1 ~~ sb06_01_t1 | -0.021 | -0.069 | 0.027 | -0.083 | -0.856 | 0.392 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8107.171 | 8191.278 | 0.851 | 0.132 | 0.155 |
# parameters of interest
params_lcs_produ_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.093 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.763 | NA | NA |
| d_produ_1 ~1 | 0.946 | 0.474 | 1.419 | 1.456 | 3.924 | 0.000 |
| produ_t1 ~1 | 3.417 | 3.283 | 3.550 | 3.668 | 50.200 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.331 | 0.169 | 0.493 | 0.783 | 3.998 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.325 | -0.460 | -0.190 | -0.466 | -4.709 | 0.000 |
| d_produ_1 ~~ sb07_08_t1 | 0.201 | 0.054 | 0.348 | 0.254 | 2.677 | 0.007 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.899 | 1.651 | 2.148 | 1.000 | 14.992 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 6176.162 | 6260.269 | 0.916 | 0.071 | 0.063 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.880 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.574 | NA | NA |
| d_produ_1 ~1 | 0.119 | -0.185 | 0.423 | 0.478 | 0.766 | 0.444 |
| produ_t1 ~1 | 1.596 | 1.517 | 1.676 | 4.183 | 39.484 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.061 | 0.009 | 0.113 | 0.989 | 2.294 | 0.022 |
| d_produ_1 ~ produ_t1 | -0.067 | -0.271 | 0.137 | -0.103 | -0.646 | 0.518 |
| d_produ_1 ~~ sb07_08_t1 | 0.041 | -0.016 | 0.097 | 0.119 | 1.415 | 0.157 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.899 | 1.651 | 2.148 | 1.000 | 14.992 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8081.61 | 8165.717 | 0.903 | 0.087 | 0.093 |
# parameters of interest
params_lcs_respo_curr_hyp4 <- broom::tidy(fit_mi_lcs_respo_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.545 | NA | NA |
| d_respo_1 ~1 | 0.680 | 0.285 | 1.076 | 2.653 | 3.369 | 0.001 |
| respo_t1 ~1 | 3.598 | 3.494 | 3.703 | 7.363 | 67.313 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.059 | 0.012 | 0.105 | 0.891 | 2.480 | 0.013 |
| d_respo_1 ~ respo_t1 | -0.173 | -0.282 | -0.065 | -0.330 | -3.141 | 0.002 |
| d_respo_1 ~~ sb06_01_t1 | 0.011 | -0.038 | 0.060 | 0.047 | 0.457 | 0.648 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7581.434 | 7665.541 | 0.922 | 0.06 | 0.054 |
# parameters of interest
params_lcs_respo_ideal_hyp4 <- broom::tidy(fit_mi_lcs_respo_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.668 | NA | NA |
| d_respo_1 ~1 | 0.968 | 0.220 | 1.715 | 5.088 | 2.538 | 0.011 |
| respo_t1 ~1 | 4.189 | 4.080 | 4.297 | 14.439 | 75.423 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.032 | 0.002 | 0.061 | 0.874 | 2.074 | 0.038 |
| d_respo_1 ~ respo_t1 | -0.233 | -0.408 | -0.057 | -0.355 | -2.600 | 0.009 |
| d_respo_1 ~~ sb06_01_t1 | 0.015 | -0.022 | 0.052 | 0.084 | 0.800 | 0.424 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8334.214 | 8418.321 | 0.859 | 0.108 | 0.12 |
# parameters of interest
params_lcs_respo_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.075 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.597 | NA | NA |
| d_respo_1 ~1 | 0.854 | 0.424 | 1.284 | 3.157 | 3.895 | 0.000 |
| respo_t1 ~1 | 3.598 | 3.494 | 3.703 | 7.385 | 67.335 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.062 | 0.015 | 0.108 | 0.841 | 2.593 | 0.010 |
| d_respo_1 ~ respo_t1 | -0.221 | -0.339 | -0.103 | -0.399 | -3.672 | 0.000 |
| d_respo_1 ~~ sb07_09_t1 | -0.065 | -0.140 | 0.011 | -0.176 | -1.683 | 0.092 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.198 | 2.013 | 2.382 | 1.000 | 23.300 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7837.713 | 7921.82 | 0.936 | 0.054 | 0.052 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.665 | NA | NA |
| d_respo_1 ~1 | 0.966 | 0.212 | 1.720 | 5.047 | 2.512 | 0.012 |
| respo_t1 ~1 | 4.188 | 4.080 | 4.297 | 14.264 | 75.446 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.032 | 0.002 | 0.062 | 0.873 | 2.091 | 0.037 |
| d_respo_1 ~ respo_t1 | -0.232 | -0.409 | -0.055 | -0.356 | -2.574 | 0.010 |
| d_respo_1 ~~ sb07_09_t1 | -0.010 | -0.066 | 0.046 | -0.037 | -0.346 | 0.729 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.198 | 2.013 | 2.382 | 1.000 | 23.300 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8150.319 | 8234.426 | 0.952 | 0.062 | 0.084 |
# parameters of interest
params_lcs_anxie_curr_hyp4 <- broom::tidy(fit_mi_lcs_anxie_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.023 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.464 | NA | NA |
| d_anxie_1 ~1 | 0.559 | 0.242 | 0.875 | 1.309 | 3.462 | 0.001 |
| anxie_t1 ~1 | 2.748 | 2.613 | 2.883 | 2.920 | 39.959 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.168 | 0.071 | 0.266 | 0.924 | 3.384 | 0.001 |
| d_anxie_1 ~ anxie_t1 | -0.125 | -0.234 | -0.015 | -0.275 | -2.235 | 0.025 |
| d_anxie_1 ~~ sb06_01_t1 | 0.021 | -0.076 | 0.117 | 0.050 | 0.419 | 0.675 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7723.024 | 7807.131 | 0.958 | 0.028 | 0.046 |
# parameters of interest
params_lcs_anxie_ideal_hyp4 <- broom::tidy(fit_mi_lcs_anxie_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.898 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.895 | NA | NA |
| d_anxie_1 ~1 | 1.708 | 0.298 | 3.118 | 6.680 | 2.374 | 0.018 |
| anxie_t1 ~1 | 4.596 | 4.535 | 4.658 | 17.904 | 146.723 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.056 | 0.006 | 0.106 | 0.857 | 2.213 | 0.027 |
| d_anxie_1 ~ anxie_t1 | -0.376 | -0.680 | -0.073 | -0.378 | -2.428 | 0.015 |
| d_anxie_1 ~~ sb06_01_t1 | 0.047 | -0.001 | 0.095 | 0.194 | 1.907 | 0.056 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8417.783 | 8501.89 | 0.853 | 0.115 | 0.151 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.482 | NA | NA |
| d_anxie_1 ~1 | 0.610 | 0.270 | 0.951 | 1.398 | 3.518 | 0.000 |
| anxie_t1 ~1 | 2.748 | 2.613 | 2.883 | 2.922 | 39.956 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.172 | 0.075 | 0.269 | 0.904 | 3.479 | 0.001 |
| d_anxie_1 ~ anxie_t1 | -0.144 | -0.264 | -0.024 | -0.309 | -2.345 | 0.019 |
| d_anxie_1 ~~ sb07_10_t1 | -0.024 | -0.173 | 0.125 | -0.039 | -0.319 | 0.750 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.275 | 2.040 | 2.510 | 1.000 | 18.985 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7994.889 | 8078.996 | 0.938 | 0.034 | 0.051 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.892 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.881 | NA | NA |
| d_anxie_1 ~1 | 1.629 | 0.188 | 3.071 | 6.322 | 2.215 | 0.027 |
| anxie_t1 ~1 | 4.596 | 4.535 | 4.658 | 17.604 | 146.404 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.058 | 0.007 | 0.108 | 0.868 | 2.230 | 0.026 |
| d_anxie_1 ~ anxie_t1 | -0.359 | -0.670 | -0.049 | -0.364 | -2.266 | 0.023 |
| d_anxie_1 ~~ sb07_10_t1 | 0.021 | -0.041 | 0.084 | 0.059 | 0.677 | 0.499 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.275 | 2.040 | 2.510 | 1.000 | 18.985 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8103.179 | 8187.286 | 0.905 | 0.107 | 0.118 |
# parameters of interest
params_lcs_depre_curr_hyp4 <- broom::tidy(fit_mi_lcs_depre_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.071 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.607 | NA | NA |
| d_depre_1 ~1 | 0.799 | 0.496 | 1.103 | 2.120 | 5.170 | 0.0 |
| depre_t1 ~1 | 3.037 | 2.917 | 3.158 | 4.561 | 49.367 | 0.0 |
| d_depre_1 ~~ d_depre_1 | 0.120 | 0.065 | 0.175 | 0.843 | 4.267 | 0.0 |
| d_depre_1 ~ depre_t1 | -0.225 | -0.324 | -0.125 | -0.397 | -4.410 | 0.0 |
| d_depre_1 ~~ sb06_01_t1 | -0.028 | -0.092 | 0.037 | -0.078 | -0.841 | 0.4 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.0 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.0 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 6477.437 | 6561.544 | 0.913 | 0.063 | 0.064 |
# parameters of interest
params_lcs_depre_ideal_hyp4 <- broom::tidy(fit_mi_lcs_depre_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.886 | NA | NA |
| d_depre_1 ~1 | 1.764 | 0.840 | 2.687 | 7.127 | 3.742 | 0.000 |
| depre_t1 ~1 | 4.454 | 4.384 | 4.524 | 15.417 | 124.697 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.048 | 0.013 | 0.083 | 0.782 | 2.663 | 0.008 |
| d_depre_1 ~ depre_t1 | -0.400 | -0.604 | -0.196 | -0.467 | -3.848 | 0.000 |
| d_depre_1 ~~ sb06_01_t1 | 0.016 | -0.022 | 0.054 | 0.072 | 0.822 | 0.411 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8401.66 | 8485.767 | 0.811 | 0.161 | 0.185 |
# parameters of interest
params_lcs_depre_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.180 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.774 | NA | NA |
| d_depre_1 ~1 | 1.197 | 0.794 | 1.599 | 2.756 | 5.829 | 0.000 |
| depre_t1 ~1 | 3.038 | 2.917 | 3.159 | 4.591 | 49.381 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.133 | 0.073 | 0.193 | 0.705 | 4.357 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.356 | -0.489 | -0.223 | -0.543 | -5.239 | 0.000 |
| d_depre_1 ~~ sb07_11_t1 | -0.220 | -0.348 | -0.093 | -0.374 | -3.386 | 0.001 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.609 | 2.418 | 2.799 | 1.000 | 26.880 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 6791.179 | 6875.286 | 0.901 | 0.067 | 0.067 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.035 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.884 | NA | NA |
| d_depre_1 ~1 | 1.755 | 0.811 | 2.699 | 7.063 | 3.645 | 0.000 |
| depre_t1 ~1 | 4.454 | 4.384 | 4.524 | 15.308 | 124.716 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.048 | 0.013 | 0.084 | 0.783 | 2.676 | 0.007 |
| d_depre_1 ~ depre_t1 | -0.398 | -0.606 | -0.190 | -0.466 | -3.750 | 0.000 |
| d_depre_1 ~~ sb07_11_t1 | -0.022 | -0.074 | 0.031 | -0.061 | -0.811 | 0.417 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.609 | 2.418 | 2.799 | 1.000 | 26.880 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8147.906 | 8232.013 | 0.965 | 0.061 | 0.086 |
# parameters of interest
params_lcs_volat_curr_hyp4 <- broom::tidy(fit_mi_lcs_volat_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.162 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.671 | NA | NA |
| d_volat_1 ~1 | 0.747 | 0.464 | 1.030 | 1.312 | 5.178 | 0.000 |
| volat_t1 ~1 | 3.117 | 2.978 | 3.256 | 3.161 | 43.889 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.238 | 0.148 | 0.329 | 0.736 | 5.145 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.296 | -0.386 | -0.207 | -0.514 | -6.480 | 0.000 |
| d_volat_1 ~~ sb06_01_t1 | 0.015 | -0.067 | 0.097 | 0.030 | 0.354 | 0.724 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7261.696 | 7345.803 | 0.957 | 0.041 | 0.052 |
# parameters of interest
params_lcs_volat_ideal_hyp4 <- broom::tidy(fit_mi_lcs_volat_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.847 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.644 | NA | NA |
| d_volat_1 ~1 | 0.193 | -0.146 | 0.533 | 0.604 | 1.116 | 0.264 |
| volat_t1 ~1 | 1.599 | 1.523 | 1.674 | 3.803 | 41.347 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.101 | 0.024 | 0.178 | 0.985 | 2.560 | 0.010 |
| d_volat_1 ~ volat_t1 | -0.092 | -0.316 | 0.131 | -0.121 | -0.809 | 0.419 |
| d_volat_1 ~~ sb06_01_t1 | -0.032 | -0.088 | 0.025 | -0.098 | -1.101 | 0.271 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8383.689 | 8467.796 | 0.959 | 0.066 | 0.092 |
# parameters of interest
params_lcs_volat_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.192 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.712 | NA | NA |
| d_volat_1 ~1 | 0.839 | 0.550 | 1.128 | 1.420 | 5.692 | 0.000 |
| volat_t1 ~1 | 3.117 | 2.978 | 3.256 | 3.151 | 43.873 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.245 | 0.152 | 0.338 | 0.702 | 5.181 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.326 | -0.418 | -0.234 | -0.546 | -6.955 | 0.000 |
| d_volat_1 ~~ sb07_12_t1 | 0.139 | 0.007 | 0.270 | 0.193 | 2.071 | 0.038 |
| sb07_12_t1 ~~ sb07_12_t1 | 2.104 | 1.911 | 2.296 | 1.000 | 21.424 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7502.824 | 7586.931 | 0.971 | 0.034 | 0.044 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.840 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.633 | NA | NA |
| d_volat_1 ~1 | 0.166 | -0.172 | 0.504 | 0.525 | 0.961 | 0.336 |
| volat_t1 ~1 | 1.599 | 1.523 | 1.674 | 3.812 | 41.368 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.099 | 0.022 | 0.175 | 0.990 | 2.517 | 0.012 |
| d_volat_1 ~ volat_t1 | -0.075 | -0.298 | 0.147 | -0.100 | -0.664 | 0.507 |
| d_volat_1 ~~ sb07_12_t1 | 0.043 | -0.041 | 0.126 | 0.094 | 1.005 | 0.315 |
| sb07_12_t1 ~~ sb07_12_t1 | 2.104 | 1.911 | 2.296 | 1.000 | 21.424 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7963.513 | 8047.62 | 0.989 | 0.026 | 0.04 |
# parameters of interest
params_lcs_curio_curr_hyp4 <- broom::tidy(fit_mi_lcs_curio_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.433 | NA | NA |
| d_curio_1 ~1 | 0.408 | -0.207 | 1.023 | 2.147 | 1.299 | 0.194 |
| curio_t1 ~1 | 4.182 | 4.095 | 4.269 | 9.590 | 93.797 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.035 | 0.001 | 0.069 | 0.959 | 1.992 | 0.046 |
| d_curio_1 ~ curio_t1 | -0.089 | -0.231 | 0.054 | -0.203 | -1.214 | 0.225 |
| d_curio_1 ~~ sb06_01_t1 | -0.007 | -0.050 | 0.036 | -0.037 | -0.316 | 0.752 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7937.656 | 8021.763 | 0.926 | 0.052 | 0.057 |
# parameters of interest
params_lcs_curio_ideal_hyp4 <- broom::tidy(fit_mi_lcs_curio_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.467 | NA | NA |
| d_curio_1 ~1 | 0.446 | -0.498 | 1.389 | 3.182 | 0.926 | 0.355 |
| curio_t1 ~1 | 4.536 | 4.465 | 4.606 | 15.423 | 126.046 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.019 | -0.006 | 0.043 | 0.962 | 1.503 | 0.133 |
| d_curio_1 ~ curio_t1 | -0.093 | -0.298 | 0.112 | -0.195 | -0.889 | 0.374 |
| d_curio_1 ~~ sb06_01_t1 | 0.027 | -0.013 | 0.067 | 0.193 | 1.322 | 0.186 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8136.233 | 8220.34 | 0.953 | 0.054 | 0.076 |
# parameters of interest
params_lcs_curio_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.420 | NA | NA |
| d_curio_1 ~1 | 0.348 | -0.293 | 0.988 | 1.880 | 1.064 | 0.287 |
| curio_t1 ~1 | 4.182 | 4.095 | 4.269 | 9.666 | 93.832 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.033 | -0.001 | 0.067 | 0.970 | 1.910 | 0.056 |
| d_curio_1 ~ curio_t1 | -0.074 | -0.223 | 0.075 | -0.174 | -0.975 | 0.329 |
| d_curio_1 ~~ sb07_13_t1 | 0.020 | -0.044 | 0.084 | 0.083 | 0.608 | 0.543 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.720 | 1.527 | 1.912 | 1.000 | 17.527 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8112.848 | 8196.955 | 0.951 | 0.042 | 0.054 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.466 | NA | NA |
| d_curio_1 ~1 | 0.439 | -0.503 | 1.382 | 3.083 | 0.914 | 0.361 |
| curio_t1 ~1 | 4.536 | 4.465 | 4.607 | 15.149 | 125.972 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.020 | -0.006 | 0.045 | 0.963 | 1.493 | 0.135 |
| d_curio_1 ~ curio_t1 | -0.092 | -0.297 | 0.113 | -0.193 | -0.876 | 0.381 |
| d_curio_1 ~~ sb07_13_t1 | -0.001 | -0.049 | 0.048 | -0.004 | -0.027 | 0.978 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.720 | 1.527 | 1.912 | 1.000 | 17.527 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8170.972 | 8255.079 | 1 | 0 | 0.031 |
# parameters of interest
params_lcs_aesth_curr_hyp4 <- broom::tidy(fit_mi_lcs_aesth_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.000 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.489 | NA | NA |
| d_aesth_1 ~1 | 0.342 | 0.065 | 0.620 | 3.455 | 2.415 | 0.016 |
| aesth_t1 ~1 | 2.934 | 2.810 | 3.058 | 14.461 | 46.387 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.009 | -0.005 | 0.024 | 0.940 | 1.253 | 0.210 |
| d_aesth_1 ~ aesth_t1 | -0.119 | -0.216 | -0.023 | -0.244 | -2.429 | 0.015 |
| d_aesth_1 ~~ sb06_01_t1 | 0.019 | -0.002 | 0.040 | 0.190 | 1.735 | 0.083 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8063.697 | 8147.804 | 1 | 0 | 0.032 |
# parameters of interest
params_lcs_aesth_ideal_hyp4 <- broom::tidy(fit_mi_lcs_aesth_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.545 | NA | NA |
| d_aesth_1 ~1 | 0.420 | -0.094 | 0.934 | 36.092 | 1.602 | 0.109 |
| aesth_t1 ~1 | 3.064 | 2.927 | 3.201 | 145.780 | 43.857 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | 0.939 | 2.356 | 0.018 |
| d_aesth_1 ~ aesth_t1 | -0.137 | -0.304 | 0.030 | -0.248 | -1.609 | 0.108 |
| d_aesth_1 ~~ sb06_01_t1 | -0.002 | -0.004 | 0.001 | -0.146 | -1.470 | 0.142 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8393.414 | 8477.521 | 1 | 0.002 | 0.035 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_aesth_1 ~1 | 0.345 | 0.065 | 0.624 | 3.495 | 2.415 | 0.016 |
| aesth_t1 ~1 | 2.934 | 2.810 | 3.058 | 14.520 | 46.389 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.009 | -0.005 | 0.023 | 0.940 | 1.249 | 0.212 |
| d_aesth_1 ~ aesth_t1 | -0.120 | -0.217 | -0.023 | -0.246 | -2.426 | 0.015 |
| d_aesth_1 ~~ sb07_14_t1 | 0.004 | -0.020 | 0.027 | 0.027 | 0.292 | 0.771 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.960 | 1.782 | 2.138 | 1.000 | 21.633 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8282.657 | 8366.764 | 0.977 | 0.037 | 0.058 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_aesth_1 ~1 | 0.434 | -0.097 | 0.966 | 40.628 | 1.601 | 0.109 |
| aesth_t1 ~1 | 3.064 | 2.927 | 3.201 | 158.344 | 43.860 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | 0.934 | 2.335 | 0.020 |
| d_aesth_1 ~ aesth_t1 | -0.142 | -0.314 | 0.031 | -0.257 | -1.608 | 0.108 |
| d_aesth_1 ~~ sb07_14_t1 | 0.001 | -0.002 | 0.004 | 0.073 | 0.705 | 0.481 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.960 | 1.782 | 2.138 | 1.000 | 21.633 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7786.61 | 7870.717 | 0.962 | 0.056 | 0.054 |
# parameters of interest
params_lcs_imagi_curr_hyp4 <- broom::tidy(fit_mi_lcs_imagi_curr_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.489 | NA | NA |
| d_imagi_1 ~1 | 0.612 | 0.195 | 1.029 | 1.752 | 2.878 | 0.004 |
| imagi_t1 ~1 | 3.644 | 3.544 | 3.744 | 4.978 | 71.253 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.112 | 0.045 | 0.179 | 0.917 | 3.267 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.138 | -0.247 | -0.028 | -0.288 | -2.453 | 0.014 |
| d_imagi_1 ~~ sb06_01_t1 | -0.015 | -0.080 | 0.051 | -0.044 | -0.440 | 0.660 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb06_01_t1 = general change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7251.188 | 7335.295 | 0.939 | 0.048 | 0.054 |
# parameters of interest
params_lcs_imagi_ideal_hyp4 <- broom::tidy(fit_mi_lcs_imagi_ideal_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb06_01_t1", "sb06_01_t1 ~~ sb06_01_t1", "sb06_01_t1 ~1 ", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.069 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.743 | NA | NA |
| d_imagi_1 ~1 | 1.424 | 0.376 | 2.472 | 5.407 | 2.663 | 0.008 |
| imagi_t1 ~1 | 4.611 | 4.549 | 4.673 | 12.173 | 146.025 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.056 | 0.021 | 0.092 | 0.809 | 3.096 | 0.002 |
| d_imagi_1 ~ imagi_t1 | -0.304 | -0.528 | -0.080 | -0.438 | -2.665 | 0.008 |
| d_imagi_1 ~~ sb06_01_t1 | 0.010 | -0.034 | 0.053 | 0.041 | 0.447 | 0.655 |
| sb06_01_t1 ~~ sb06_01_t1 | 1.029 | 0.883 | 1.175 | 1.000 | 13.805 | 0.000 |
| sb06_01_t1 ~1 | 3.059 | 2.951 | 3.168 | 3.016 | 55.362 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 8010.164 | 8094.271 | 0.915 | 0.085 | 0.098 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp4 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.044 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.512 | NA | NA |
| d_imagi_1 ~1 | 0.701 | 0.269 | 1.132 | 1.958 | 3.185 | 0.001 |
| imagi_t1 ~1 | 3.644 | 3.544 | 3.745 | 4.996 | 71.264 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.114 | 0.048 | 0.180 | 0.892 | 3.386 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.161 | -0.275 | -0.048 | -0.329 | -2.778 | 0.005 |
| d_imagi_1 ~~ sb07_15_t1 | -0.088 | -0.180 | 0.004 | -0.184 | -1.878 | 0.060 |
| sb07_15_t1 ~~ sb07_15_t1 | 2.018 | 1.837 | 2.198 | 1.000 | 21.903 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp4) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 22 | 7478.098 | 7562.205 | 0.892 | 0.065 | 0.066 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp4 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp4, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp4, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.068 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.740 | NA | NA |
| d_imagi_1 ~1 | 1.413 | 0.363 | 2.462 | 5.375 | 2.638 | 0.008 |
| imagi_t1 ~1 | 4.611 | 4.549 | 4.673 | 12.160 | 146.023 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.056 | 0.020 | 0.092 | 0.811 | 3.085 | 0.002 |
| d_imagi_1 ~ imagi_t1 | -0.302 | -0.525 | -0.078 | -0.435 | -2.640 | 0.008 |
| d_imagi_1 ~~ sb07_15_t1 | -0.014 | -0.076 | 0.048 | -0.042 | -0.443 | 0.658 |
| sb07_15_t1 ~~ sb07_15_t1 | 2.018 | 1.837 | 2.198 | 1.000 | 21.903 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp4 <- bind_rows(
#traits
params_lcs_extra_curr_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_ideal_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_curr_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_ideal_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_curr_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_ideal_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_curr_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_ideal_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_curr_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_ideal_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_curr_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_ideal_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_curr_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_socia_ideal_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_asser_curr_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_ideal_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_curr_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_asser_ideal_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_energ_curr_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_ideal_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_curr_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_energ_ideal_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_compa_curr_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_ideal_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_curr_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_compa_ideal_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_respe_curr_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_ideal_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_curr_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_respe_ideal_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_trust_curr_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_ideal_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_curr_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_trust_ideal_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_organ_curr_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_ideal_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_curr_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_organ_ideal_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_produ_curr_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_ideal_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_curr_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_produ_ideal_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_respo_curr_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_ideal_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_curr_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_respo_ideal_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_anxie_curr_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_ideal_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_curr_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_anxie_ideal_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_depre_curr_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_ideal_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_curr_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_depre_ideal_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_volat_curr_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_ideal_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_curr_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_volat_ideal_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_curio_curr_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_ideal_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_curr_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_curio_ideal_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_aesth_curr_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_ideal_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_curr_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_aesth_ideal_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_imagi_curr_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_ideal_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_curr_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
params_lcs_imagi_ideal_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[1:20, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extraversion | current | general | -0.026 | -0.097 | -1.090 | 0.276 |
| extraversion | ideal | general | 0.002 | 0.009 | 0.094 | 0.925 |
| extraversion | current | specific | -0.046 | -0.263 | -1.264 | 0.206 |
| extraversion | ideal | specific | 0.016 | 0.118 | 0.812 | 0.417 |
| agreeableness | current | general | 0.023 | 0.118 | 1.383 | 0.167 |
| agreeableness | ideal | general | 0.009 | 0.034 | 0.438 | 0.662 |
| agreeableness | current | specific | -0.014 | -0.078 | -0.728 | 0.467 |
| agreeableness | ideal | specific | -0.004 | -0.016 | -0.150 | 0.880 |
| conscientiousness | current | general | 0.022 | 0.055 | 0.619 | 0.536 |
| conscientiousness | ideal | general | 0.011 | 0.044 | 0.538 | 0.590 |
| conscientiousness | current | specific | -0.108 | -0.230 | -2.365 | 0.018 |
| conscientiousness | ideal | specific | -0.035 | -0.116 | -1.255 | 0.209 |
| neuroticism | current | general | -0.004 | -0.010 | -0.123 | 0.902 |
| neuroticism | ideal | general | -0.046 | -0.130 | -1.665 | 0.096 |
| neuroticism | current | specific | 0.135 | 0.287 | 3.053 | 0.002 |
| neuroticism | ideal | specific | 0.027 | 0.071 | 0.847 | 0.397 |
| openness | current | general | -0.016 | -0.060 | -0.692 | 0.489 |
| openness | ideal | general | 0.003 | 0.018 | 0.197 | 0.844 |
| openness | current | specific | -0.028 | -0.134 | -1.392 | 0.164 |
| openness | ideal | specific | -0.002 | -0.018 | -0.177 | 0.860 |
Results summary across the Big Five facets: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[21:80, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| sociability | current | general | -0.026 | -0.071 | -0.796 | 0.426 |
| sociability | ideal | general | 0.042 | 0.162 | 1.531 | 0.126 |
| sociability | current | specific | -0.104 | -0.229 | -2.128 | 0.033 |
| sociability | ideal | specific | -0.042 | -0.131 | -1.224 | 0.221 |
| assertiveness | current | general | -0.019 | -0.073 | -0.848 | 0.397 |
| assertiveness | ideal | general | -0.014 | -0.070 | -0.629 | 0.530 |
| assertiveness | current | specific | -0.068 | -0.200 | -2.134 | 0.033 |
| assertiveness | ideal | specific | 0.010 | 0.041 | 0.405 | 0.686 |
| energy | current | general | 0.016 | 0.112 | 0.976 | 0.329 |
| energy | ideal | general | 0.000 | 0.001 | 0.008 | 0.993 |
| energy | current | specific | -0.001 | -0.008 | -0.074 | 0.941 |
| energy | ideal | specific | 0.003 | 0.016 | 0.153 | 0.879 |
| compassion | current | general | 0.027 | 0.123 | 1.014 | 0.311 |
| compassion | ideal | general | -0.030 | -0.133 | -1.012 | 0.312 |
| compassion | current | specific | -0.010 | -0.035 | -0.264 | 0.792 |
| compassion | ideal | specific | 0.018 | 0.061 | 0.403 | 0.687 |
| respectfulness | current | general | 0.000 | 0.001 | 0.013 | 0.990 |
| respectfulness | ideal | general | 0.009 | 0.040 | 0.367 | 0.714 |
| respectfulness | current | specific | -0.066 | -0.180 | -1.915 | 0.056 |
| respectfulness | ideal | specific | -0.050 | -0.171 | -1.591 | 0.112 |
| trust | current | general | -0.035 | -0.127 | -1.089 | 0.276 |
| trust | ideal | general | -0.006 | -0.045 | -0.260 | 0.795 |
| trust | current | specific | -0.033 | -0.088 | -0.739 | 0.460 |
| trust | ideal | specific | -0.032 | -0.184 | -1.019 | 0.308 |
| organization | current | general | -0.007 | -0.015 | -0.163 | 0.871 |
| organization | ideal | general | 0.004 | 0.016 | 0.165 | 0.869 |
| organization | current | specific | 0.140 | 0.204 | 1.890 | 0.059 |
| organization | ideal | specific | 0.048 | 0.122 | 1.133 | 0.257 |
| productiveness | current | general | -0.004 | -0.007 | -0.075 | 0.940 |
| productiveness | ideal | general | -0.021 | -0.083 | -0.856 | 0.392 |
| productiveness | current | specific | 0.201 | 0.254 | 2.677 | 0.007 |
| productiveness | ideal | specific | 0.041 | 0.119 | 1.415 | 0.157 |
| responsibility | current | general | 0.011 | 0.047 | 0.457 | 0.648 |
| responsibility | ideal | general | 0.015 | 0.084 | 0.800 | 0.424 |
| responsibility | current | specific | -0.065 | -0.176 | -1.683 | 0.092 |
| responsibility | ideal | specific | -0.010 | -0.037 | -0.346 | 0.729 |
| anxiety | current | general | 0.021 | 0.050 | 0.419 | 0.675 |
| anxiety | ideal | general | 0.047 | 0.194 | 1.907 | 0.056 |
| anxiety | current | specific | -0.024 | -0.039 | -0.319 | 0.750 |
| anxiety | ideal | specific | 0.021 | 0.059 | 0.677 | 0.499 |
| depression | current | general | -0.028 | -0.078 | -0.841 | 0.400 |
| depression | ideal | general | 0.016 | 0.072 | 0.822 | 0.411 |
| depression | current | specific | -0.220 | -0.374 | -3.386 | 0.001 |
| depression | ideal | specific | -0.022 | -0.061 | -0.811 | 0.417 |
| volatility | current | general | 0.015 | 0.030 | 0.354 | 0.724 |
| volatility | ideal | general | -0.032 | -0.098 | -1.101 | 0.271 |
| volatility | current | specific | 0.139 | 0.193 | 2.071 | 0.038 |
| volatility | ideal | specific | 0.043 | 0.094 | 1.005 | 0.315 |
| curiosity | current | general | -0.007 | -0.037 | -0.316 | 0.752 |
| curiosity | ideal | general | 0.027 | 0.193 | 1.322 | 0.186 |
| curiosity | current | specific | 0.020 | 0.083 | 0.608 | 0.543 |
| curiosity | ideal | specific | -0.001 | -0.004 | -0.027 | 0.978 |
| aesthetic | current | general | 0.019 | 0.190 | 1.735 | 0.083 |
| aesthetic | ideal | general | -0.002 | -0.146 | -1.470 | 0.142 |
| aesthetic | current | specific | 0.004 | 0.027 | 0.292 | 0.771 |
| aesthetic | ideal | specific | 0.001 | 0.073 | 0.705 | 0.481 |
| imagination | current | general | -0.015 | -0.044 | -0.440 | 0.660 |
| imagination | ideal | general | 0.010 | 0.041 | 0.447 | 0.655 |
| imagination | current | specific | -0.088 | -0.184 | -1.878 | 0.060 |
| imagination | ideal | specific | -0.014 | -0.042 | -0.443 | 0.658 |
Prepare data frame for plotting:
df_table_hyp4_plot <- bind_rows(
#traits
params_lcs_extra_curr_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_ideal_hyp4 %>% filter(term=="d_extra_1 ~~ sb06_01_t1"),
params_lcs_extra_curr_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_ideal_hyp4 %>% filter(term=="d_agree_1 ~~ sb06_01_t1"),
params_lcs_agree_curr_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_ideal_hyp4 %>% filter(term=="d_consc_1 ~~ sb06_01_t1"),
params_lcs_consc_curr_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_ideal_hyp4 %>% filter(term=="d_neuro_1 ~~ sb06_01_t1"),
params_lcs_neuro_curr_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_ideal_hyp4 %>% filter(term=="d_openn_1 ~~ sb06_01_t1"),
params_lcs_openn_curr_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp4 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_ideal_hyp4 %>% filter(term=="d_socia_1 ~~ sb06_01_t1"),
params_lcs_socia_curr_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_socia_ideal_specif_hyp4 %>% filter(term=="d_socia_1 ~~ sb07_01_t1"),
params_lcs_asser_curr_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_ideal_hyp4 %>% filter(term=="d_asser_1 ~~ sb06_01_t1"),
params_lcs_asser_curr_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_asser_ideal_specif_hyp4 %>% filter(term=="d_asser_1 ~~ sb07_02_t1"),
params_lcs_energ_curr_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_ideal_hyp4 %>% filter(term=="d_energ_1 ~~ sb06_01_t1"),
params_lcs_energ_curr_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_energ_ideal_specif_hyp4 %>% filter(term=="d_energ_1 ~~ sb07_03_t1"),
params_lcs_compa_curr_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_ideal_hyp4 %>% filter(term=="d_compa_1 ~~ sb06_01_t1"),
params_lcs_compa_curr_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_compa_ideal_specif_hyp4 %>% filter(term=="d_compa_1 ~~ sb07_04_t1"),
params_lcs_respe_curr_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_ideal_hyp4 %>% filter(term=="d_respe_1 ~~ sb06_01_t1"),
params_lcs_respe_curr_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_respe_ideal_specif_hyp4 %>% filter(term=="d_respe_1 ~~ sb07_05_t1"),
params_lcs_trust_curr_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_ideal_hyp4 %>% filter(term=="d_trust_1 ~~ sb06_01_t1"),
params_lcs_trust_curr_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_trust_ideal_specif_hyp4 %>% filter(term=="d_trust_1 ~~ sb07_06_t1"),
params_lcs_organ_curr_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_ideal_hyp4 %>% filter(term=="d_organ_1 ~~ sb06_01_t1"),
params_lcs_organ_curr_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_organ_ideal_specif_hyp4 %>% filter(term=="d_organ_1 ~~ sb07_07_t1"),
params_lcs_produ_curr_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_ideal_hyp4 %>% filter(term=="d_produ_1 ~~ sb06_01_t1"),
params_lcs_produ_curr_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_produ_ideal_specif_hyp4 %>% filter(term=="d_produ_1 ~~ sb07_08_t1"),
params_lcs_respo_curr_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_ideal_hyp4 %>% filter(term=="d_respo_1 ~~ sb06_01_t1"),
params_lcs_respo_curr_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_respo_ideal_specif_hyp4 %>% filter(term=="d_respo_1 ~~ sb07_09_t1"),
params_lcs_anxie_curr_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_ideal_hyp4 %>% filter(term=="d_anxie_1 ~~ sb06_01_t1"),
params_lcs_anxie_curr_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_anxie_ideal_specif_hyp4 %>% filter(term=="d_anxie_1 ~~ sb07_10_t1"),
params_lcs_depre_curr_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_ideal_hyp4 %>% filter(term=="d_depre_1 ~~ sb06_01_t1"),
params_lcs_depre_curr_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_depre_ideal_specif_hyp4 %>% filter(term=="d_depre_1 ~~ sb07_11_t1"),
params_lcs_volat_curr_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_ideal_hyp4 %>% filter(term=="d_volat_1 ~~ sb06_01_t1"),
params_lcs_volat_curr_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_volat_ideal_specif_hyp4 %>% filter(term=="d_volat_1 ~~ sb07_12_t1"),
params_lcs_curio_curr_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_ideal_hyp4 %>% filter(term=="d_curio_1 ~~ sb06_01_t1"),
params_lcs_curio_curr_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_curio_ideal_specif_hyp4 %>% filter(term=="d_curio_1 ~~ sb07_13_t1"),
params_lcs_aesth_curr_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_ideal_hyp4 %>% filter(term=="d_aesth_1 ~~ sb06_01_t1"),
params_lcs_aesth_curr_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_aesth_ideal_specif_hyp4 %>% filter(term=="d_aesth_1 ~~ sb07_14_t1"),
params_lcs_imagi_curr_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_ideal_hyp4 %>% filter(term=="d_imagi_1 ~~ sb06_01_t1"),
params_lcs_imagi_curr_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
params_lcs_imagi_ideal_specif_hyp4 %>% filter(term=="d_imagi_1 ~~ sb07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp4_plot <- df_table_hyp4_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor(ref, levels = c("current", "ideal"), labels = c("current", "ideal"))) %>%
mutate(goal = factor(goal, levels = c("general", "specific"), labels = c("general", "specific"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp4_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + goal, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Not specifically preregistered for study 2 because we were foremost interested in the moderation hypothesis (H7 in prereg / H3 in paper) and thought that it was somewhat redundant with these models of correlations with the change score. I still ran the models for study 2 here (without the control group and for changes between T1 and T2) in case the comparison with study 1 is of interest.
In the self-acceptance group, there will be a correlation between acceptance goals and change in ideal-self ratings but not change in current-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
Reshape and split data set by intervention group:
# from T1 to T2
df_sbsa2_wide_pers_sa <- df_sbsa2 %>%
filter(rando=="Self-Acceptance" & time %in% c(1,2)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07")), # facet-specific change goals
sa06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sa07"), sa06_01)) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2, sa06_01_t2))
# colnames(df_sbsa2_wide_pers_sa)
group_assign <- df_sbsa2 %>% select(pid, rando) %>% unique()
df_sbsa2_wide_pers_sa <- df_sbsa2_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance") %>% select(-rando, -ends_with("_t3")) %>%
left_join(df_sbsa2_wide_pers_sa)
# follow-up, from T2 to T3 (for later)
df_sbsa2_wide_pers_sa_fu <- df_sbsa2 %>%
filter(rando=="Self-Acceptance" & time %in% c(2,3)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07")), # facet-specific change goals
sa06_01) %>% # general change goal
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with("sa07"), sa06_01)) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2, sa06_01_t2))
# colnames(df_sbsa2_wide_pers_sa)
df_sbsa2_wide_pers_sa_fu <- df_sbsa2_wide_pers %>% left_join(group_assign) %>% filter(rando=="Self-Acceptance" & !is.na(valid_t2)) %>% select(-rando, -ends_with("_t1")) %>%
left_join(df_sbsa2_wide_pers_sa_fu)Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_hyp5 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_extra_curr_hyp5 <- lavaan(mi_lcs_extra_curr_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 4384.367 | 4453.075 | 0.992 | 0.041 | 0.043 |
# parameters of interest
params_lcs_extra_curr_hyp5 <- broom::tidy(fit_mi_lcs_extra_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.449 | NA | NA |
| d_extra_1 ~1 | 0.453 | 0.238 | 0.668 | 1.736 | 4.123 | 0.000 |
| extra_t1 ~1 | 3.032 | 2.944 | 3.119 | 5.092 | 67.772 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.063 | 0.032 | 0.094 | 0.925 | 3.952 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.120 | -0.190 | -0.050 | -0.274 | -3.373 | 0.001 |
| d_extra_1 ~~ sa06_01_t1 | 0.008 | -0.028 | 0.044 | 0.037 | 0.446 | 0.656 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_hyp5 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_extra_ideal_hyp5 <- lavaan(mi_lcs_extra_ideal_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3855.205 | 3923.913 | 0.96 | 0.06 | 0.071 |
# parameters of interest
params_lcs_extra_ideal_hyp5 <- broom::tidy(fit_mi_lcs_extra_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.646 | NA | NA |
| d_extra_1 ~1 | 0.736 | 0.138 | 1.335 | 2.841 | 2.413 | 0.016 |
| extra_t1 ~1 | 3.689 | 3.625 | 3.752 | 9.261 | 113.383 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.061 | 0.024 | 0.098 | 0.901 | 3.209 | 0.001 |
| d_extra_1 ~ extra_t1 | -0.205 | -0.365 | -0.045 | -0.315 | -2.505 | 0.012 |
| d_extra_1 ~~ sa06_01_t1 | 0.016 | -0.024 | 0.057 | 0.073 | 0.778 | 0.437 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_curr_specif_hyp5 <- '
extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
extra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1
extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1
extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1
extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2
extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2
extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2
extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_01_t1 ~~ sa07_01_t1
sa07_02_t1 ~~ sa07_02_t1
sa07_03_t1 ~~ sa07_03_t1
sa07_01_t1 ~ 1
sa07_02_t1 ~ 1
sa07_03_t1 ~ 1
'
fit_mi_lcs_extra_curr_specif_hyp5 <- lavaan(mi_lcs_extra_curr_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6877.957 | 6973.384 | 0.919 | 0.107 | 0.155 |
# parameters of interest
params_lcs_extra_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.472 | NA | NA |
| d_extra_1 ~1 | 0.533 | 0.298 | 0.767 | 1.989 | 4.454 | 0.000 |
| extra_t1 ~1 | 3.032 | 2.944 | 3.119 | 5.098 | 67.832 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.064 | 0.032 | 0.096 | 0.894 | 3.960 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.146 | -0.222 | -0.071 | -0.325 | -3.806 | 0.000 |
| goals ~~ d_extra_1 | -0.035 | -0.092 | 0.023 | -0.155 | -1.181 | 0.238 |
| goals ~~ goals | 0.771 | 0.373 | 1.169 | 1.000 | 3.799 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_extra_ideal_specif_hyp5 <- '
extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1
extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
goals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)
extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1
d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2
extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0
extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0
d_extra_1 ~ 1 # This estimates the intercept of the change score
extra_t1 ~ 1 # This estimates the intercept of extra_t1
d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores
extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1
d_extra_1 ~ extra_t1 # This estimates the self-feedback parameter
d_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
extra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_01_t1 ~~ sa07_01_t1
sa07_02_t1 ~~ sa07_02_t1
sa07_03_t1 ~~ sa07_03_t1
sa07_01_t1 ~ 1
sa07_02_t1 ~ 1
sa07_03_t1 ~ 1
'
fit_mi_lcs_extra_ideal_specif_hyp5 <- lavaan(mi_lcs_extra_ideal_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_extra_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6350.889 | 6446.316 | 0.994 | 0.019 | 0.045 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"goals ~~ d_extra_1", "goals ~~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.635 | NA | NA |
| d_extra_1 ~1 | 0.688 | 0.100 | 1.277 | 2.664 | 2.294 | 0.022 |
| extra_t1 ~1 | 3.689 | 3.625 | 3.753 | 9.207 | 113.364 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.061 | 0.023 | 0.098 | 0.911 | 3.176 | 0.001 |
| d_extra_1 ~ extra_t1 | -0.192 | -0.350 | -0.034 | -0.298 | -2.386 | 0.017 |
| goals ~~ d_extra_1 | -0.011 | -0.058 | 0.037 | -0.050 | -0.443 | 0.658 |
| goals ~~ goals | 0.752 | 0.356 | 1.148 | 1.000 | 3.723 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_hyp5 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_agree_curr_hyp5 <- lavaan(mi_lcs_agree_curr_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3727.854 | 3796.562 | 0.996 | 0.023 | 0.034 |
# parameters of interest
params_lcs_agree_curr_hyp5 <- broom::tidy(fit_mi_lcs_agree_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.965 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.520 | NA | NA |
| d_agree_1 ~1 | 0.402 | -0.030 | 0.833 | 1.994 | 1.823 | 0.068 |
| agree_t1 ~1 | 3.461 | 3.398 | 3.524 | 9.256 | 107.763 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.039 | 0.010 | 0.068 | 0.959 | 2.637 | 0.008 |
| d_agree_1 ~ agree_t1 | -0.109 | -0.232 | 0.015 | -0.202 | -1.724 | 0.085 |
| d_agree_1 ~~ sa06_01_t1 | -0.008 | -0.032 | 0.017 | -0.043 | -0.605 | 0.545 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_hyp5 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_agree_ideal_hyp5 <- lavaan(mi_lcs_agree_ideal_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3719.683 | 3788.391 | 0.99 | 0.039 | 0.046 |
# parameters of interest
params_lcs_agree_ideal_hyp5 <- broom::tidy(fit_mi_lcs_agree_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.621 | NA | NA |
| d_agree_1 ~1 | 0.762 | 0.297 | 1.226 | 2.771 | 3.213 | 0.001 |
| agree_t1 ~1 | 3.778 | 3.705 | 3.851 | 8.441 | 101.667 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.068 | 0.040 | 0.095 | 0.894 | 4.740 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.200 | -0.319 | -0.081 | -0.326 | -3.294 | 0.001 |
| d_agree_1 ~~ sa06_01_t1 | 0.000 | -0.036 | 0.036 | -0.002 | -0.020 | 0.984 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_curr_specif_hyp5 <- '
agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
agree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1
agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1
agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1
agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2
agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2
agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2
agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_04_t1 ~~ sa07_04_t1
sa07_05_t1 ~~ sa07_05_t1
sa07_06_t1 ~~ sa07_06_t1
sa07_04_t1 ~ 1
sa07_05_t1 ~ 1
sa07_06_t1 ~ 1
'
fit_mi_lcs_agree_curr_specif_hyp5 <- lavaan(mi_lcs_agree_curr_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6064.576 | 6160.003 | 0.967 | 0.061 | 0.084 |
# parameters of interest
params_lcs_agree_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.000 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.563 | NA | NA |
| d_agree_1 ~1 | 0.572 | 0.128 | 1.016 | 2.760 | 2.526 | 0.012 |
| agree_t1 ~1 | 3.462 | 3.399 | 3.525 | 9.401 | 107.744 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.040 | 0.011 | 0.069 | 0.921 | 2.675 | 0.007 |
| d_agree_1 ~ agree_t1 | -0.158 | -0.286 | -0.031 | -0.281 | -2.434 | 0.015 |
| goals ~~ d_agree_1 | -0.059 | -0.107 | -0.011 | -0.309 | -2.392 | 0.017 |
| goals ~~ goals | 0.919 | 0.632 | 1.207 | 1.000 | 6.262 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_agree_ideal_specif_hyp5 <- '
agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1
agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadings
goals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)
agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1
d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2
agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0
agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0
d_agree_1 ~ 1 # This estimates the intercept of the change score
agree_t1 ~ 1 # This estimates the intercept of agree_t1
d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores
agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1
d_agree_1 ~ agree_t1 # This estimates the self-feedback parameter
d_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
agree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_04_t1 ~~ sa07_04_t1
sa07_05_t1 ~~ sa07_05_t1
sa07_06_t1 ~~ sa07_06_t1
sa07_04_t1 ~ 1
sa07_05_t1 ~ 1
sa07_06_t1 ~ 1
'
fit_mi_lcs_agree_ideal_specif_hyp5 <- lavaan(mi_lcs_agree_ideal_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_agree_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6067.194 | 6162.622 | 0.987 | 0.04 | 0.052 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"goals ~~ d_agree_1", "goals ~~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.630 | NA | NA |
| d_agree_1 ~1 | 0.794 | 0.317 | 1.271 | 2.874 | 3.263 | 0.001 |
| agree_t1 ~1 | 3.778 | 3.705 | 3.851 | 8.461 | 101.680 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.068 | 0.040 | 0.096 | 0.886 | 4.756 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.209 | -0.331 | -0.086 | -0.337 | -3.337 | 0.001 |
| goals ~~ d_agree_1 | -0.024 | -0.069 | 0.020 | -0.100 | -1.087 | 0.277 |
| goals ~~ goals | 0.884 | 0.564 | 1.204 | 1.000 | 5.417 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_hyp5 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_consc_curr_hyp5 <- lavaan(mi_lcs_consc_curr_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3974.434 | 4043.142 | 0.997 | 0.026 | 0.032 |
# parameters of interest
params_lcs_consc_curr_hyp5 <- broom::tidy(fit_mi_lcs_consc_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.514 | NA | NA |
| d_consc_1 ~1 | 0.486 | 0.243 | 0.729 | 1.394 | 3.921 | 0.000 |
| consc_t1 ~1 | 3.380 | 3.299 | 3.461 | 5.058 | 81.598 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.115 | 0.078 | 0.152 | 0.945 | 6.045 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.122 | -0.191 | -0.053 | -0.234 | -3.457 | 0.001 |
| d_consc_1 ~~ sa06_01_t1 | -0.031 | -0.075 | 0.012 | -0.104 | -1.418 | 0.156 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_hyp5 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_consc_ideal_hyp5 <- lavaan(mi_lcs_consc_ideal_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3299.599 | 3368.307 | 0.994 | 0.031 | 0.041 |
# parameters of interest
params_lcs_consc_ideal_hyp5 <- broom::tidy(fit_mi_lcs_consc_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.744 | NA | NA |
| d_consc_1 ~1 | 1.294 | 0.702 | 1.887 | 4.101 | 4.280 | 0.000 |
| consc_t1 ~1 | 4.339 | 4.279 | 4.400 | 9.869 | 141.201 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.083 | 0.050 | 0.116 | 0.834 | 4.882 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.292 | -0.425 | -0.160 | -0.407 | -4.320 | 0.000 |
| d_consc_1 ~~ sa06_01_t1 | 0.017 | -0.015 | 0.049 | 0.065 | 1.044 | 0.297 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_curr_specif_hyp5 <- '
consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
consc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1
consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1
consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1
consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2
consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2
consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2
consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_07_t1 ~~ sa07_07_t1
sa07_08_t1 ~~ sa07_08_t1
sa07_09_t1 ~~ sa07_09_t1
sa07_07_t1 ~ 1
sa07_08_t1 ~ 1
sa07_09_t1 ~ 1
'
fit_mi_lcs_consc_curr_specif_hyp5 <- lavaan(mi_lcs_consc_curr_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6378.707 | 6474.135 | 0.921 | 0.125 | 0.214 |
# parameters of interest
params_lcs_consc_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.548 | NA | NA |
| d_consc_1 ~1 | 0.621 | 0.325 | 0.918 | 1.726 | 4.102 | 0.000 |
| consc_t1 ~1 | 3.380 | 3.299 | 3.461 | 5.051 | 81.584 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.118 | 0.081 | 0.155 | 0.909 | 6.204 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.162 | -0.247 | -0.077 | -0.301 | -3.736 | 0.000 |
| goals ~~ d_consc_1 | -0.062 | -0.132 | 0.009 | -0.167 | -1.707 | 0.088 |
| goals ~~ goals | 1.152 | 0.853 | 1.450 | 1.000 | 7.563 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_consc_ideal_specif_hyp5 <- '
consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1
consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadings
goals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)
consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1
d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2
consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0
consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0
d_consc_1 ~ 1 # This estimates the intercept of the change score
consc_t1 ~ 1 # This estimates the intercept of consc_t1
d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores
consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1
d_consc_1 ~ consc_t1 # This estimates the self-feedback parameter
d_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
consc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_07_t1 ~~ sa07_07_t1
sa07_08_t1 ~~ sa07_08_t1
sa07_09_t1 ~~ sa07_09_t1
sa07_07_t1 ~ 1
sa07_08_t1 ~ 1
sa07_09_t1 ~ 1
'
fit_mi_lcs_consc_ideal_specif_hyp5 <- lavaan(mi_lcs_consc_ideal_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_consc_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 5701.826 | 5797.254 | 1 | 0 | 0.032 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"goals ~~ d_consc_1", "goals ~~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.037 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.744 | NA | NA |
| d_consc_1 ~1 | 1.294 | 0.697 | 1.891 | 4.095 | 4.246 | 0.000 |
| consc_t1 ~1 | 4.339 | 4.279 | 4.400 | 9.859 | 141.196 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.083 | 0.050 | 0.117 | 0.834 | 4.863 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.292 | -0.426 | -0.159 | -0.407 | -4.288 | 0.000 |
| goals ~~ d_consc_1 | -0.050 | -0.098 | -0.002 | -0.162 | -2.036 | 0.042 |
| goals ~~ goals | 1.135 | 0.838 | 1.433 | 1.000 | 7.475 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_hyp5 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_neuro_curr_hyp5 <- lavaan(mi_lcs_neuro_curr_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 4184.13 | 4252.838 | 0.996 | 0.033 | 0.077 |
# parameters of interest
params_lcs_neuro_curr_hyp5 <- broom::tidy(fit_mi_lcs_neuro_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.458 | NA | NA |
| d_neuro_1 ~1 | 0.124 | -0.080 | 0.328 | 0.334 | 1.195 | 0.232 |
| neuro_t1 ~1 | 3.250 | 3.158 | 3.342 | 4.068 | 68.935 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.133 | 0.090 | 0.175 | 0.960 | 6.079 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.093 | -0.154 | -0.031 | -0.199 | -2.957 | 0.003 |
| d_neuro_1 ~~ sa06_01_t1 | -0.023 | -0.065 | 0.019 | -0.071 | -1.082 | 0.279 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_hyp5 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_hyp5 <- lavaan(mi_lcs_neuro_ideal_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3513.93 | 3582.638 | 0.987 | 0.042 | 0.06 |
# parameters of interest
params_lcs_neuro_ideal_hyp5 <- broom::tidy(fit_mi_lcs_neuro_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.071 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.705 | NA | NA |
| d_neuro_1 ~1 | 0.485 | 0.271 | 0.699 | 1.382 | 4.451 | 0.000 |
| neuro_t1 ~1 | 1.751 | 1.685 | 1.818 | 3.285 | 51.768 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.101 | 0.057 | 0.144 | 0.818 | 4.565 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.281 | -0.412 | -0.149 | -0.427 | -4.186 | 0.000 |
| d_neuro_1 ~~ sa06_01_t1 | -0.016 | -0.061 | 0.030 | -0.056 | -0.681 | 0.496 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_curr_specif_hyp5 <- '
neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
neuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_10_t1 ~~ sa07_10_t1
sa07_11_t1 ~~ sa07_11_t1
sa07_12_t1 ~~ sa07_12_t1
sa07_10_t1 ~ 1
sa07_11_t1 ~ 1
sa07_12_t1 ~ 1
'
fit_mi_lcs_neuro_curr_specif_hyp5 <- lavaan(mi_lcs_neuro_curr_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6594.582 | 6690.01 | 0.955 | 0.097 | 0.184 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.021 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.492 | NA | NA |
| d_neuro_1 ~1 | 0.266 | 0.034 | 0.499 | 0.692 | 2.248 | 0.025 |
| neuro_t1 ~1 | 3.250 | 3.158 | 3.343 | 4.069 | 68.935 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.136 | 0.093 | 0.179 | 0.920 | 6.170 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.137 | -0.207 | -0.067 | -0.283 | -3.826 | 0.000 |
| goals ~~ d_neuro_1 | 0.066 | 0.005 | 0.127 | 0.186 | 2.121 | 0.034 |
| goals ~~ goals | 0.932 | 0.615 | 1.248 | 1.000 | 5.774 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_neuro_ideal_specif_hyp5 <- '
neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1
neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadings
goals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)
neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1
d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2
neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0
neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0
d_neuro_1 ~ 1 # This estimates the intercept of the change score
neuro_t1 ~ 1 # This estimates the intercept of neuro_t1
d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores
neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1
d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameter
d_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
neuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_10_t1 ~~ sa07_10_t1
sa07_11_t1 ~~ sa07_11_t1
sa07_12_t1 ~~ sa07_12_t1
sa07_10_t1 ~ 1
sa07_11_t1 ~ 1
sa07_12_t1 ~ 1
'
fit_mi_lcs_neuro_ideal_specif_hyp5 <- lavaan(mi_lcs_neuro_ideal_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_neuro_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 5928.655 | 6024.083 | 0.99 | 0.033 | 0.051 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"goals ~~ d_neuro_1", "goals ~~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.066 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.699 | NA | NA |
| d_neuro_1 ~1 | 0.475 | 0.260 | 0.690 | 1.359 | 4.326 | 0.000 |
| neuro_t1 ~1 | 1.751 | 1.685 | 1.818 | 3.282 | 51.764 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.101 | 0.057 | 0.144 | 0.824 | 4.553 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.275 | -0.407 | -0.143 | -0.420 | -4.075 | 0.000 |
| goals ~~ d_neuro_1 | 0.003 | -0.046 | 0.052 | 0.009 | 0.116 | 0.908 |
| goals ~~ goals | 0.939 | 0.618 | 1.260 | 1.000 | 5.732 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_hyp5 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_openn_curr_hyp5 <- lavaan(mi_lcs_openn_curr_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3881.553 | 3950.261 | 1 | 0 | 0.031 |
# parameters of interest
params_lcs_openn_curr_hyp5 <- broom::tidy(fit_mi_lcs_openn_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.405 | NA | NA |
| d_openn_1 ~1 | 0.422 | 0.171 | 0.672 | 1.764 | 3.301 | 0.001 |
| openn_t1 ~1 | 3.564 | 3.489 | 3.639 | 5.935 | 93.254 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.054 | 0.025 | 0.082 | 0.941 | 3.656 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.097 | -0.164 | -0.030 | -0.244 | -2.820 | 0.005 |
| d_openn_1 ~~ sa06_01_t1 | -0.014 | -0.048 | 0.020 | -0.067 | -0.794 | 0.427 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with manifest acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_hyp5 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa06_01_t1 ~~ sa06_01_t1
sa06_01_t1 ~ 1
'
fit_mi_lcs_openn_ideal_hyp5 <- lavaan(mi_lcs_openn_ideal_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing="fiml")
summary(fit_mi_lcs_openn_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 18 | 3782.839 | 3851.547 | 0.968 | 0.063 | 0.063 |
# parameters of interest
params_lcs_openn_ideal_hyp5 <- broom::tidy(fit_mi_lcs_openn_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.033 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.711 | NA | NA |
| d_openn_1 ~1 | 1.063 | 0.582 | 1.544 | 5.008 | 4.331 | 0.000 |
| openn_t1 ~1 | 3.907 | 3.848 | 3.966 | 12.679 | 130.315 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.038 | 0.017 | 0.059 | 0.848 | 3.590 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.268 | -0.389 | -0.147 | -0.390 | -4.339 | 0.000 |
| d_openn_1 ~~ sa06_01_t1 | -0.025 | -0.050 | 0.000 | -0.143 | -1.941 | 0.052 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_curr_specif_hyp5 <- '
openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
openn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1
openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1
openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1
openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2
openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2
openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2
openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_13_t1 ~~ sa07_13_t1
sa07_14_t1 ~~ sa07_14_t1
sa07_15_t1 ~~ sa07_15_t1
sa07_13_t1 ~ 1
sa07_14_t1 ~ 1
sa07_15_t1 ~ 1
'
fit_mi_lcs_openn_curr_specif_hyp5 <- lavaan(mi_lcs_openn_curr_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
summary(fit_mi_lcs_openn_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6103.116 | 6198.544 | 0.95 | 0.096 | 0.185 |
# parameters of interest
params_lcs_openn_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.428 | NA | NA |
| d_openn_1 ~1 | 0.516 | 0.218 | 0.814 | 2.092 | 3.395 | 0.001 |
| openn_t1 ~1 | 3.564 | 3.489 | 3.639 | 5.937 | 93.257 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.055 | 0.026 | 0.084 | 0.910 | 3.732 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.123 | -0.206 | -0.041 | -0.301 | -2.938 | 0.003 |
| goals ~~ d_openn_1 | -0.024 | -0.067 | 0.019 | -0.133 | -1.099 | 0.272 |
| goals ~~ goals | 0.599 | 0.383 | 0.815 | 1.000 | 5.436 | 0.000 |
Fit model:
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:
# Fit the multiple indicator univariate latent change score model
mi_lcs_openn_ideal_specif_hyp5 <- '
openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1
openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadings
goals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)
openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1
d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2
openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0
openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0
d_openn_1 ~ 1 # This estimates the intercept of the change score
openn_t1 ~ 1 # This estimates the intercept of openn_t1
d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores
openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1
d_openn_1 ~ openn_t1 # This estimates the self-feedback parameter
d_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variable
goals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0
goals ~~ goals # This estimates the variance of the (latent) acceptance goal variable
openn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1
openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1
openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1
openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2
openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2
openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2
openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa07_13_t1 ~~ sa07_13_t1
sa07_14_t1 ~~ sa07_14_t1
sa07_15_t1 ~~ sa07_15_t1
sa07_13_t1 ~ 1
sa07_14_t1 ~ 1
sa07_15_t1 ~ 1
'
fit_mi_lcs_openn_ideal_specif_hyp5 <- lavaan(mi_lcs_openn_ideal_specif_hyp5, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing = "fiml")
summary(fit_mi_lcs_openn_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 25 | 6005.375 | 6100.803 | 0.96 | 0.069 | 0.107 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"goals ~~ d_openn_1", "goals ~~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.069 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.769 | NA | NA |
| d_openn_1 ~1 | 1.271 | 0.770 | 1.771 | 5.780 | 4.977 | 0.000 |
| openn_t1 ~1 | 3.907 | 3.848 | 3.966 | 12.792 | 130.356 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.039 | 0.018 | 0.060 | 0.800 | 3.620 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.321 | -0.448 | -0.195 | -0.447 | -4.977 | 0.000 |
| goals ~~ d_openn_1 | -0.024 | -0.046 | -0.002 | -0.157 | -2.101 | 0.036 |
| goals ~~ goals | 0.600 | 0.384 | 0.815 | 1.000 | 5.454 | 0.000 |
Run models for all facets with a template & loop:
# create template:
facet_template <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
d_facet_1 ~ facet_t1 # This estimates the self-feedback parameter
d_facet_1 ~~ ind_goal # estimates the covariance/correlation with acceptance goal variable
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# loop across 2 BFI versions (combined pre&post current/ideal)
for (j in 5:length(bfi_versions)) {
items = paste0(bfi_versions[[j]], item_nrs)
# loop across 2 different goal operationalizations (sa06_01_t1 & sa07_XX_t1)
for (k in 1:2) {
if (k==1) {
goal_op = "sa06_01_t1"
} else{
goal_op = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
}
template_filled <- str_replace_all(facet_template,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = goal_op))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')
# save to environment
if (k==1) {
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp5")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_hyp5")), facet_model_fit))
} else{
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), facet_model_fit))
}
}
}
} Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7725.646 | 7809.623 | 0.953 | 0.078 | 0.05 |
# parameters of interest
params_lcs_socia_curr_hyp5 <- broom::tidy(fit_mi_lcs_socia_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.475 | NA | NA |
| d_socia_1 ~1 | 0.629 | 0.427 | 0.831 | 1.447 | 6.095 | 0.000 |
| socia_t1 ~1 | 2.858 | 2.724 | 2.992 | 2.912 | 41.737 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.163 | 0.083 | 0.243 | 0.864 | 3.990 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.163 | -0.229 | -0.098 | -0.369 | -4.909 | 0.000 |
| d_socia_1 ~~ sa06_01_t1 | 0.032 | -0.040 | 0.104 | 0.090 | 0.879 | 0.379 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7789.622 | 7873.599 | 0.88 | 0.064 | 0.071 |
# parameters of interest
params_lcs_socia_ideal_hyp5 <- broom::tidy(fit_mi_lcs_socia_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.888 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.475 | NA | NA |
| d_socia_1 ~1 | -0.026 | -1.165 | 1.113 | -0.142 | -0.045 | 0.964 |
| socia_t1 ~1 | 4.226 | 4.145 | 4.306 | 12.175 | 102.771 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.034 | -0.021 | 0.090 | 1.000 | 1.220 | 0.222 |
| d_socia_1 ~ socia_t1 | -0.008 | -0.274 | 0.257 | -0.015 | -0.061 | 0.951 |
| d_socia_1 ~~ sa06_01_t1 | -0.020 | -0.071 | 0.032 | -0.118 | -0.742 | 0.458 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8003.301 | 8087.277 | 0.907 | 0.112 | 0.136 |
# parameters of interest
params_lcs_socia_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.099 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.505 | NA | NA |
| d_socia_1 ~1 | 0.707 | 0.482 | 0.933 | 1.573 | 6.145 | 0.000 |
| socia_t1 ~1 | 2.858 | 2.724 | 2.993 | 2.919 | 41.771 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.167 | 0.086 | 0.248 | 0.826 | 4.043 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.192 | -0.265 | -0.119 | -0.417 | -5.145 | 0.000 |
| d_socia_1 ~~ sa07_01_t1 | -0.066 | -0.166 | 0.035 | -0.119 | -1.284 | 0.199 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.826 | 1.634 | 2.019 | 1.000 | 18.622 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8065.135 | 8149.111 | 0.922 | 0.051 | 0.06 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.888 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.468 | NA | NA |
| d_socia_1 ~1 | -0.037 | -1.134 | 1.060 | -0.203 | -0.066 | 0.947 |
| socia_t1 ~1 | 4.225 | 4.144 | 4.305 | 12.225 | 102.942 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.033 | -0.021 | 0.088 | 1.000 | 1.196 | 0.232 |
| d_socia_1 ~ socia_t1 | -0.005 | -0.261 | 0.251 | -0.010 | -0.041 | 0.967 |
| d_socia_1 ~~ sa07_01_t1 | -0.059 | -0.125 | 0.007 | -0.239 | -1.742 | 0.082 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.826 | 1.634 | 2.019 | 1.000 | 18.622 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7839.177 | 7923.154 | 0.992 | 0.028 | 0.035 |
# parameters of interest
params_lcs_asser_curr_hyp5 <- broom::tidy(fit_mi_lcs_asser_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.373 | NA | NA |
| d_asser_1 ~1 | 0.260 | 0.026 | 0.494 | 0.994 | 2.179 | 0.029 |
| asser_t1 ~1 | 3.105 | 2.986 | 3.224 | 4.442 | 51.156 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.066 | 0.020 | 0.113 | 0.968 | 2.803 | 0.005 |
| d_asser_1 ~ asser_t1 | -0.067 | -0.143 | 0.010 | -0.178 | -1.713 | 0.087 |
| d_asser_1 ~~ sa06_01_t1 | -0.009 | -0.050 | 0.031 | -0.041 | -0.457 | 0.648 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7719.373 | 7803.349 | 0.972 | 0.033 | 0.057 |
# parameters of interest
params_lcs_asser_ideal_hyp5 <- broom::tidy(fit_mi_lcs_asser_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.935 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.435 | NA | NA |
| d_asser_1 ~1 | 0.124 | -0.816 | 1.065 | 0.751 | 0.259 | 0.796 |
| asser_t1 ~1 | 4.134 | 4.043 | 4.224 | 11.645 | 89.478 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.027 | -0.011 | 0.066 | 0.994 | 1.378 | 0.168 |
| d_asser_1 ~ asser_t1 | -0.036 | -0.260 | 0.188 | -0.077 | -0.315 | 0.753 |
| d_asser_1 ~~ sa06_01_t1 | 0.035 | -0.003 | 0.073 | 0.240 | 1.826 | 0.068 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8102.542 | 8186.519 | 0.958 | 0.063 | 0.087 |
# parameters of interest
params_lcs_asser_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.374 | NA | NA |
| d_asser_1 ~1 | 0.265 | 0.023 | 0.508 | 1.011 | 2.145 | 0.032 |
| asser_t1 ~1 | 3.105 | 2.986 | 3.224 | 4.433 | 51.158 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.067 | 0.020 | 0.113 | 0.967 | 2.798 | 0.005 |
| d_asser_1 ~ asser_t1 | -0.068 | -0.147 | 0.011 | -0.182 | -1.686 | 0.092 |
| d_asser_1 ~~ sa07_02_t1 | -0.006 | -0.078 | 0.066 | -0.018 | -0.164 | 0.870 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.747 | 1.573 | 1.921 | 1.000 | 19.717 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7981.585 | 8065.561 | 1 | 0 | 0.045 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.930 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.436 | NA | NA |
| d_asser_1 ~1 | 0.106 | -0.835 | 1.047 | 0.636 | 0.221 | 0.825 |
| asser_t1 ~1 | 4.134 | 4.044 | 4.225 | 11.625 | 89.537 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.028 | -0.011 | 0.066 | 0.995 | 1.401 | 0.161 |
| d_asser_1 ~ asser_t1 | -0.032 | -0.256 | 0.192 | -0.068 | -0.278 | 0.781 |
| d_asser_1 ~~ sa07_02_t1 | 0.060 | -0.005 | 0.125 | 0.273 | 1.817 | 0.069 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.747 | 1.573 | 1.921 | 1.000 | 19.717 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7987.215 | 8071.191 | 0.971 | 0.047 | 0.055 |
# parameters of interest
params_lcs_energ_curr_hyp5 <- broom::tidy(fit_mi_lcs_energ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.019 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_energ_1 ~1 | 0.310 | 0.057 | 0.563 | 1.692 | 2.405 | 0.016 |
| energ_t1 ~1 | 2.676 | 2.563 | 2.788 | 6.989 | 46.612 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.031 | 0.001 | 0.061 | 0.922 | 2.042 | 0.041 |
| d_energ_1 ~ energ_t1 | -0.133 | -0.228 | -0.039 | -0.279 | -2.763 | 0.006 |
| d_energ_1 ~~ sa06_01_t1 | 0.003 | -0.021 | 0.027 | 0.019 | 0.245 | 0.806 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7431.315 | 7515.291 | 0.891 | 0.057 | 0.066 |
# parameters of interest
params_lcs_energ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_energ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.009 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.780 | NA | NA |
| d_energ_1 ~1 | 0.685 | 0.289 | 1.082 | 4.047 | 3.385 | 0.001 |
| energ_t1 ~1 | 2.221 | 2.105 | 2.338 | 10.133 | 37.361 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.024 | -0.014 | 0.063 | 0.841 | 1.225 | 0.221 |
| d_energ_1 ~ energ_t1 | -0.308 | -0.488 | -0.127 | -0.398 | -3.345 | 0.001 |
| d_energ_1 ~~ sa06_01_t1 | -0.009 | -0.033 | 0.014 | -0.067 | -0.781 | 0.435 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8267.686 | 8351.662 | 0.903 | 0.09 | 0.113 |
# parameters of interest
params_lcs_energ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.049 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.523 | NA | NA |
| d_energ_1 ~1 | 0.407 | 0.137 | 0.678 | 2.131 | 2.952 | 0.003 |
| energ_t1 ~1 | 2.676 | 2.563 | 2.789 | 6.987 | 46.602 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.032 | 0.002 | 0.063 | 0.884 | 2.063 | 0.039 |
| d_energ_1 ~ energ_t1 | -0.170 | -0.272 | -0.068 | -0.341 | -3.260 | 0.001 |
| d_energ_1 ~~ sa07_03_t1 | 0.038 | -0.015 | 0.090 | 0.155 | 1.415 | 0.157 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.851 | 1.650 | 2.052 | 1.000 | 18.028 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7714.43 | 7798.407 | 0.921 | 0.048 | 0.057 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.005 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.769 | NA | NA |
| d_energ_1 ~1 | 0.663 | 0.263 | 1.064 | 3.936 | 3.247 | 0.001 |
| energ_t1 ~1 | 2.221 | 2.105 | 2.338 | 10.085 | 37.352 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.024 | -0.014 | 0.062 | 0.849 | 1.234 | 0.217 |
| d_energ_1 ~ energ_t1 | -0.298 | -0.480 | -0.115 | -0.389 | -3.197 | 0.001 |
| d_energ_1 ~~ sa07_03_t1 | -0.007 | -0.043 | 0.029 | -0.035 | -0.398 | 0.690 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.851 | 1.650 | 2.052 | 1.000 | 18.028 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7766.535 | 7850.511 | 0.972 | 0.043 | 0.049 |
# parameters of interest
params_lcs_compa_curr_hyp5 <- broom::tidy(fit_mi_lcs_compa_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.892 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.546 | NA | NA |
| d_compa_1 ~1 | 0.300 | -0.399 | 0.999 | 0.769 | 0.841 | 0.401 |
| compa_t1 ~1 | 4.213 | 4.123 | 4.303 | 6.607 | 91.772 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.151 | 0.049 | 0.253 | 0.991 | 2.898 | 0.004 |
| d_compa_1 ~ compa_t1 | -0.059 | -0.219 | 0.100 | -0.097 | -0.729 | 0.466 |
| d_compa_1 ~~ sa06_01_t1 | -0.039 | -0.100 | 0.023 | -0.112 | -1.236 | 0.216 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8035.384 | 8119.36 | 0.971 | 0.034 | 0.048 |
# parameters of interest
params_lcs_compa_ideal_hyp5 <- broom::tidy(fit_mi_lcs_compa_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.604 | NA | NA |
| d_compa_1 ~1 | 0.843 | -0.035 | 1.722 | 2.295 | 1.882 | 0.060 |
| compa_t1 ~1 | 4.291 | 4.196 | 4.385 | 6.936 | 89.023 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.121 | 0.022 | 0.219 | 0.895 | 2.407 | 0.016 |
| d_compa_1 ~ compa_t1 | -0.192 | -0.390 | 0.006 | -0.323 | -1.904 | 0.057 |
| d_compa_1 ~~ sa06_01_t1 | -0.005 | -0.069 | 0.060 | -0.015 | -0.138 | 0.890 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8074.403 | 8158.38 | 0.942 | 0.063 | 0.076 |
# parameters of interest
params_lcs_compa_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.919 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.586 | NA | NA |
| d_compa_1 ~1 | 0.515 | -0.209 | 1.239 | 1.263 | 1.394 | 0.163 |
| compa_t1 ~1 | 4.213 | 4.123 | 4.303 | 6.578 | 91.771 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.161 | 0.055 | 0.268 | 0.969 | 2.968 | 0.003 |
| d_compa_1 ~ compa_t1 | -0.111 | -0.278 | 0.056 | -0.175 | -1.308 | 0.191 |
| d_compa_1 ~~ sa07_04_t1 | -0.131 | -0.257 | -0.006 | -0.230 | -2.053 | 0.040 |
| sa07_04_t1 ~~ sa07_04_t1 | 2.024 | 1.813 | 2.235 | 1.000 | 18.799 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8346.947 | 8430.924 | 0.945 | 0.047 | 0.058 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_compa_1 ~1 | 0.914 | 0.018 | 1.810 | 2.428 | 2.000 | 0.045 |
| compa_t1 ~1 | 4.291 | 4.196 | 4.385 | 6.934 | 88.972 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.125 | 0.026 | 0.223 | 0.882 | 2.487 | 0.013 |
| d_compa_1 ~ compa_t1 | -0.209 | -0.411 | -0.007 | -0.344 | -2.027 | 0.043 |
| d_compa_1 ~~ sa07_04_t1 | -0.060 | -0.173 | 0.053 | -0.119 | -1.036 | 0.300 |
| sa07_04_t1 ~~ sa07_04_t1 | 2.024 | 1.813 | 2.235 | 1.000 | 18.799 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6758.117 | 6842.094 | 0.958 | 0.056 | 0.059 |
# parameters of interest
params_lcs_respe_curr_hyp5 <- broom::tidy(fit_mi_lcs_respe_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_respe_1 ~1 | 0.892 | 0.143 | 1.642 | 2.685 | 2.334 | 0.020 |
| respe_t1 ~1 | 4.444 | 4.374 | 4.514 | 8.449 | 124.133 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.100 | 0.029 | 0.171 | 0.904 | 2.758 | 0.006 |
| d_respe_1 ~ respe_t1 | -0.195 | -0.359 | -0.032 | -0.309 | -2.344 | 0.019 |
| d_respe_1 ~~ sa06_01_t1 | -0.007 | -0.049 | 0.035 | -0.023 | -0.308 | 0.758 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6216.963 | 6300.939 | 0.932 | 0.065 | 0.061 |
# parameters of interest
params_lcs_respe_ideal_hyp5 <- broom::tidy(fit_mi_lcs_respe_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.386 | NA | NA |
| d_respe_1 ~1 | 0.329 | -0.482 | 1.140 | 2.253 | 0.795 | 0.426 |
| respe_t1 ~1 | 4.625 | 4.560 | 4.690 | 12.258 | 139.827 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.021 | -0.004 | 0.045 | 0.966 | 1.624 | 0.104 |
| d_respe_1 ~ respe_t1 | -0.071 | -0.242 | 0.099 | -0.184 | -0.820 | 0.412 |
| d_respe_1 ~~ sa06_01_t1 | -0.004 | -0.040 | 0.032 | -0.030 | -0.209 | 0.834 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6992.382 | 7076.358 | 0.929 | 0.076 | 0.1 |
# parameters of interest
params_lcs_respe_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.065 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.742 | NA | NA |
| d_respe_1 ~1 | 1.363 | 0.642 | 2.083 | 3.716 | 3.708 | 0.000 |
| respe_t1 ~1 | 4.444 | 4.373 | 4.514 | 8.442 | 124.033 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.109 | 0.035 | 0.184 | 0.812 | 2.873 | 0.004 |
| d_respe_1 ~ respe_t1 | -0.302 | -0.460 | -0.143 | -0.433 | -3.731 | 0.000 |
| d_respe_1 ~~ sa07_05_t1 | -0.195 | -0.294 | -0.095 | -0.446 | -3.818 | 0.000 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.744 | 1.473 | 2.015 | 1.000 | 12.613 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6478.347 | 6562.324 | 0.905 | 0.078 | 0.066 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.004 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.398 | NA | NA |
| d_respe_1 ~1 | 0.382 | -0.426 | 1.190 | 2.531 | 0.926 | 0.354 |
| respe_t1 ~1 | 4.625 | 4.560 | 4.690 | 12.144 | 139.684 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.022 | -0.004 | 0.048 | 0.956 | 1.643 | 0.100 |
| d_respe_1 ~ respe_t1 | -0.083 | -0.253 | 0.087 | -0.209 | -0.954 | 0.340 |
| d_respe_1 ~~ sa07_05_t1 | -0.030 | -0.091 | 0.030 | -0.156 | -0.984 | 0.325 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.744 | 1.473 | 2.015 | 1.000 | 12.613 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8045.446 | 8129.423 | 0.97 | 0.046 | 0.052 |
# parameters of interest
params_lcs_trust_curr_hyp5 <- broom::tidy(fit_mi_lcs_trust_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.313 | NA | NA |
| d_trust_1 ~1 | 0.137 | -0.181 | 0.454 | 0.636 | 0.844 | 0.399 |
| trust_t1 ~1 | 2.850 | 2.732 | 2.968 | 3.994 | 47.430 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.043 | -0.026 | 0.111 | 0.927 | 1.225 | 0.220 |
| d_trust_1 ~ trust_t1 | -0.082 | -0.190 | 0.027 | -0.271 | -1.472 | 0.141 |
| d_trust_1 ~~ sa06_01_t1 | -0.007 | -0.058 | 0.043 | -0.040 | -0.283 | 0.777 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7760.181 | 7844.158 | 0.967 | 0.037 | 0.044 |
# parameters of interest
params_lcs_trust_ideal_hyp5 <- broom::tidy(fit_mi_lcs_trust_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.107 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.597 | NA | NA |
| d_trust_1 ~1 | 0.448 | 0.166 | 0.730 | 1.623 | 3.110 | 0.002 |
| trust_t1 ~1 | 1.946 | 1.847 | 2.045 | 3.806 | 38.539 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.061 | 0.003 | 0.120 | 0.806 | 2.065 | 0.039 |
| d_trust_1 ~ trust_t1 | -0.238 | -0.385 | -0.091 | -0.440 | -3.168 | 0.002 |
| d_trust_1 ~~ sa06_01_t1 | -0.008 | -0.053 | 0.036 | -0.038 | -0.371 | 0.710 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8337.224 | 8421.2 | 0.944 | 0.064 | 0.075 |
# parameters of interest
params_lcs_trust_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.296 | NA | NA |
| d_trust_1 ~1 | 0.093 | -0.231 | 0.417 | 0.450 | 0.563 | 0.573 |
| trust_t1 ~1 | 2.850 | 2.732 | 2.968 | 3.982 | 47.411 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.040 | -0.028 | 0.109 | 0.947 | 1.152 | 0.249 |
| d_trust_1 ~ trust_t1 | -0.066 | -0.177 | 0.045 | -0.229 | -1.172 | 0.241 |
| d_trust_1 ~~ sa07_06_t1 | -0.055 | -0.153 | 0.042 | -0.198 | -1.108 | 0.268 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.909 | 1.711 | 2.108 | 1.000 | 18.829 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8053.016 | 8136.993 | 0.967 | 0.037 | 0.043 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.106 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.595 | NA | NA |
| d_trust_1 ~1 | 0.445 | 0.165 | 0.726 | 1.626 | 3.110 | 0.002 |
| trust_t1 ~1 | 1.946 | 1.847 | 2.045 | 3.819 | 38.550 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.061 | 0.002 | 0.119 | 0.807 | 2.037 | 0.042 |
| d_trust_1 ~ trust_t1 | -0.236 | -0.382 | -0.090 | -0.439 | -3.167 | 0.002 |
| d_trust_1 ~~ sa07_06_t1 | -0.025 | -0.102 | 0.052 | -0.073 | -0.629 | 0.529 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.909 | 1.711 | 2.108 | 1.000 | 18.829 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7703.447 | 7787.423 | 1 | 0 | 0.022 |
# parameters of interest
params_lcs_organ_curr_hyp5 <- broom::tidy(fit_mi_lcs_organ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.509 | NA | NA |
| d_organ_1 ~1 | 0.325 | 0.064 | 0.586 | 0.699 | 2.445 | 0.014 |
| organ_t1 ~1 | 2.963 | 2.828 | 3.098 | 3.233 | 43.052 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.202 | 0.106 | 0.297 | 0.933 | 4.150 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.131 | -0.220 | -0.043 | -0.259 | -2.916 | 0.004 |
| d_organ_1 ~~ sa06_01_t1 | 0.074 | 0.006 | 0.142 | 0.185 | 2.143 | 0.032 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6690.012 | 6773.989 | 0.979 | 0.028 | 0.049 |
# parameters of interest
params_lcs_organ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_organ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.971 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.603 | NA | NA |
| d_organ_1 ~1 | 0.280 | -0.106 | 0.667 | 1.067 | 1.421 | 0.155 |
| organ_t1 ~1 | 1.754 | 1.651 | 1.857 | 4.149 | 33.466 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.064 | 0.013 | 0.116 | 0.931 | 2.441 | 0.015 |
| d_organ_1 ~ organ_t1 | -0.163 | -0.396 | 0.070 | -0.262 | -1.370 | 0.171 |
| d_organ_1 ~~ sa06_01_t1 | 0.003 | -0.040 | 0.046 | 0.013 | 0.139 | 0.889 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8017.535 | 8101.511 | 0.919 | 0.096 | 0.143 |
# parameters of interest
params_lcs_organ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.601 | NA | NA |
| d_organ_1 ~1 | 0.593 | 0.286 | 0.899 | 1.144 | 3.787 | 0.000 |
| organ_t1 ~1 | 2.964 | 2.829 | 3.098 | 3.206 | 43.031 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.226 | 0.126 | 0.326 | 0.842 | 4.414 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.222 | -0.325 | -0.120 | -0.397 | -4.270 | 0.000 |
| d_organ_1 ~~ sa07_07_t1 | 0.213 | 0.087 | 0.338 | 0.311 | 3.323 | 0.001 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.070 | 1.888 | 2.251 | 1.000 | 22.344 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7008.38 | 7092.356 | 0.98 | 0.027 | 0.046 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.973 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.607 | NA | NA |
| d_organ_1 ~1 | 0.286 | -0.094 | 0.667 | 1.083 | 1.476 | 0.140 |
| organ_t1 ~1 | 1.754 | 1.651 | 1.857 | 4.136 | 33.453 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.065 | 0.013 | 0.117 | 0.929 | 2.438 | 0.015 |
| d_organ_1 ~ organ_t1 | -0.166 | -0.395 | 0.063 | -0.266 | -1.424 | 0.154 |
| d_organ_1 ~~ sa07_07_t1 | 0.051 | -0.028 | 0.130 | 0.139 | 1.259 | 0.208 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.070 | 1.888 | 2.251 | 1.000 | 22.344 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7607.317 | 7691.293 | 0.965 | 0.058 | 0.049 |
# parameters of interest
params_lcs_produ_curr_hyp5 <- broom::tidy(fit_mi_lcs_produ_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.962 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.544 | NA | NA |
| d_produ_1 ~1 | 0.284 | -0.047 | 0.616 | 0.724 | 1.680 | 0.093 |
| produ_t1 ~1 | 3.428 | 3.299 | 3.556 | 4.929 | 52.312 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.147 | 0.076 | 0.219 | 0.955 | 4.041 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.120 | -0.217 | -0.023 | -0.212 | -2.420 | 0.016 |
| d_produ_1 ~~ sa06_01_t1 | 0.031 | -0.024 | 0.087 | 0.092 | 1.111 | 0.267 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6087.909 | 6171.886 | 0.909 | 0.068 | 0.069 |
# parameters of interest
params_lcs_produ_ideal_hyp5 <- broom::tidy(fit_mi_lcs_produ_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.045 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.675 | NA | NA |
| d_produ_1 ~1 | 0.367 | 0.110 | 0.625 | 1.546 | 2.800 | 0.005 |
| produ_t1 ~1 | 1.585 | 1.507 | 1.663 | 4.307 | 39.898 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.048 | 0.009 | 0.087 | 0.850 | 2.408 | 0.016 |
| d_produ_1 ~ produ_t1 | -0.250 | -0.424 | -0.076 | -0.388 | -2.821 | 0.005 |
| d_produ_1 ~~ sa06_01_t1 | -0.006 | -0.035 | 0.024 | -0.029 | -0.374 | 0.708 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7892.577 | 7976.554 | 0.879 | 0.113 | 0.137 |
# parameters of interest
params_lcs_produ_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.002 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_produ_1 ~1 | 0.484 | 0.080 | 0.889 | 1.178 | 2.348 | 0.019 |
| produ_t1 ~1 | 3.428 | 3.299 | 3.556 | 4.940 | 52.301 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.154 | 0.080 | 0.228 | 0.911 | 4.090 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.177 | -0.294 | -0.060 | -0.299 | -2.975 | 0.003 |
| d_produ_1 ~~ sa07_08_t1 | 0.100 | -0.007 | 0.207 | 0.186 | 1.833 | 0.067 |
| sa07_08_t1 ~~ sa07_08_t1 | 1.880 | 1.691 | 2.069 | 1.000 | 19.477 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6372.494 | 6456.471 | 0.911 | 0.068 | 0.066 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.047 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.677 | NA | NA |
| d_produ_1 ~1 | 0.373 | 0.116 | 0.629 | 1.559 | 2.849 | 0.004 |
| produ_t1 ~1 | 1.585 | 1.507 | 1.663 | 4.286 | 39.886 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.048 | 0.009 | 0.088 | 0.847 | 2.413 | 0.016 |
| d_produ_1 ~ produ_t1 | -0.253 | -0.426 | -0.080 | -0.391 | -2.866 | 0.004 |
| d_produ_1 ~~ sa07_08_t1 | 0.050 | -0.005 | 0.105 | 0.166 | 1.789 | 0.074 |
| sa07_08_t1 ~~ sa07_08_t1 | 1.880 | 1.691 | 2.069 | 1.000 | 19.477 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7705.316 | 7789.293 | 0.871 | 0.104 | 0.093 |
# parameters of interest
params_lcs_respo_curr_hyp5 <- broom::tidy(fit_mi_lcs_respo_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.510 | NA | NA |
| d_respo_1 ~1 | 0.544 | 0.179 | 0.908 | 2.970 | 2.925 | 0.003 |
| respo_t1 ~1 | 3.659 | 3.557 | 3.760 | 10.024 | 70.694 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.031 | 0.001 | 0.060 | 0.918 | 2.051 | 0.040 |
| d_respo_1 ~ respo_t1 | -0.143 | -0.241 | -0.046 | -0.286 | -2.885 | 0.004 |
| d_respo_1 ~~ sa06_01_t1 | -0.009 | -0.037 | 0.018 | -0.060 | -0.668 | 0.504 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6906.534 | 6990.51 | 0.964 | 0.044 | 0.053 |
# parameters of interest
params_lcs_respo_ideal_hyp5 <- broom::tidy(fit_mi_lcs_respo_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.917 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_respo_1 ~1 | 0.612 | -0.296 | 1.520 | 2.222 | 1.322 | 0.186 |
| respo_t1 ~1 | 4.238 | 4.134 | 4.342 | 10.536 | 79.921 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.073 | 0.019 | 0.127 | 0.958 | 2.648 | 0.008 |
| d_respo_1 ~ respo_t1 | -0.140 | -0.348 | 0.069 | -0.204 | -1.315 | 0.188 |
| d_respo_1 ~~ sa06_01_t1 | 0.035 | -0.006 | 0.076 | 0.147 | 1.685 | 0.092 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8022.652 | 8106.628 | 0.813 | 0.129 | 0.136 |
# parameters of interest
params_lcs_respo_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.558 | NA | NA |
| d_respo_1 ~1 | 0.713 | 0.296 | 1.129 | 3.667 | 3.350 | 0.001 |
| respo_t1 ~1 | 3.659 | 3.557 | 3.760 | 9.965 | 70.676 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.033 | 0.002 | 0.064 | 0.872 | 2.096 | 0.036 |
| d_respo_1 ~ respo_t1 | -0.189 | -0.302 | -0.077 | -0.358 | -3.310 | 0.001 |
| d_respo_1 ~~ sa07_09_t1 | -0.048 | -0.111 | 0.015 | -0.185 | -1.505 | 0.132 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.069 | 1.860 | 2.278 | 1.000 | 19.415 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7226.383 | 7310.359 | 0.958 | 0.047 | 0.052 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.917 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.630 | NA | NA |
| d_respo_1 ~1 | 0.613 | -0.294 | 1.521 | 2.289 | 1.324 | 0.185 |
| respo_t1 ~1 | 4.239 | 4.135 | 4.343 | 10.867 | 79.965 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.069 | 0.017 | 0.121 | 0.958 | 2.593 | 0.010 |
| d_respo_1 ~ respo_t1 | -0.141 | -0.349 | 0.068 | -0.205 | -1.323 | 0.186 |
| d_respo_1 ~~ sa07_09_t1 | -0.059 | -0.127 | 0.009 | -0.155 | -1.689 | 0.091 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.069 | 1.860 | 2.278 | 1.000 | 19.415 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7886.698 | 7970.675 | 0.983 | 0.04 | 0.068 |
# parameters of interest
params_lcs_anxie_curr_hyp5 <- broom::tidy(fit_mi_lcs_anxie_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.976 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.423 | NA | NA |
| d_anxie_1 ~1 | 0.387 | 0.133 | 0.641 | 0.939 | 2.988 | 0.003 |
| anxie_t1 ~1 | 2.877 | 2.745 | 3.008 | 3.031 | 42.808 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.165 | 0.073 | 0.258 | 0.975 | 3.508 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.069 | -0.159 | 0.021 | -0.159 | -1.495 | 0.135 |
| d_anxie_1 ~~ sa06_01_t1 | 0.027 | -0.042 | 0.096 | 0.075 | 0.778 | 0.437 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7670.394 | 7754.371 | 0.923 | 0.049 | 0.061 |
# parameters of interest
params_lcs_anxie_ideal_hyp5 <- broom::tidy(fit_mi_lcs_anxie_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.185 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.739 | NA | NA |
| d_anxie_1 ~1 | 1.554 | 0.519 | 2.588 | 6.098 | 2.943 | 0.003 |
| anxie_t1 ~1 | 4.530 | 4.451 | 4.610 | 11.094 | 111.900 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.046 | 0.005 | 0.086 | 0.705 | 2.222 | 0.026 |
| d_anxie_1 ~ anxie_t1 | -0.339 | -0.562 | -0.115 | -0.543 | -2.969 | 0.003 |
| d_anxie_1 ~~ sa06_01_t1 | 0.012 | -0.040 | 0.065 | 0.065 | 0.469 | 0.639 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8207.505 | 8291.481 | 0.952 | 0.068 | 0.105 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.022 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_anxie_1 ~1 | 0.560 | 0.290 | 0.830 | 1.274 | 4.069 | 0.000 |
| anxie_t1 ~1 | 2.877 | 2.746 | 3.009 | 3.048 | 42.841 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.178 | 0.086 | 0.271 | 0.923 | 3.778 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.130 | -0.223 | -0.036 | -0.278 | -2.717 | 0.007 |
| d_anxie_1 ~~ sa07_10_t1 | -0.165 | -0.287 | -0.043 | -0.269 | -2.652 | 0.008 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.115 | 1.921 | 2.308 | 1.000 | 21.431 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7998.05 | 8082.026 | 0.924 | 0.049 | 0.058 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.180 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.730 | NA | NA |
| d_anxie_1 ~1 | 1.525 | 0.487 | 2.563 | 6.027 | 2.880 | 0.004 |
| anxie_t1 ~1 | 4.530 | 4.451 | 4.610 | 11.075 | 111.886 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.046 | 0.005 | 0.086 | 0.711 | 2.210 | 0.027 |
| d_anxie_1 ~ anxie_t1 | -0.332 | -0.557 | -0.108 | -0.537 | -2.906 | 0.004 |
| d_anxie_1 ~~ sa07_10_t1 | -0.013 | -0.079 | 0.053 | -0.042 | -0.387 | 0.698 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.115 | 1.921 | 2.308 | 1.000 | 21.431 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7801.948 | 7885.924 | 0.946 | 0.081 | 0.077 |
# parameters of interest
params_lcs_depre_curr_hyp5 <- broom::tidy(fit_mi_lcs_depre_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.504 | NA | NA |
| d_depre_1 ~1 | 0.473 | 0.231 | 0.714 | 1.669 | 3.838 | 0.000 |
| depre_t1 ~1 | 3.026 | 2.909 | 3.143 | 5.476 | 50.760 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.076 | 0.041 | 0.111 | 0.950 | 4.293 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.115 | -0.195 | -0.034 | -0.223 | -2.800 | 0.005 |
| d_depre_1 ~~ sa06_01_t1 | 0.006 | -0.030 | 0.042 | 0.025 | 0.327 | 0.744 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6096.322 | 6180.299 | 0.897 | 0.072 | 0.075 |
# parameters of interest
params_lcs_depre_ideal_hyp5 <- broom::tidy(fit_mi_lcs_depre_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.157 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.701 | NA | NA |
| d_depre_1 ~1 | 1.383 | 0.649 | 2.118 | 5.223 | 3.692 | 0.000 |
| depre_t1 ~1 | 4.443 | 4.368 | 4.519 | 10.154 | 115.576 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.052 | 0.011 | 0.093 | 0.738 | 2.463 | 0.014 |
| d_depre_1 ~ depre_t1 | -0.310 | -0.471 | -0.149 | -0.512 | -3.775 | 0.000 |
| d_depre_1 ~~ sa06_01_t1 | 0.002 | -0.041 | 0.045 | 0.011 | 0.106 | 0.916 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8124.898 | 8208.874 | 0.905 | 0.11 | 0.129 |
# parameters of interest
params_lcs_depre_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.015 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_depre_1 ~1 | 0.598 | 0.343 | 0.853 | 2.051 | 4.599 | 0.000 |
| depre_t1 ~1 | 3.026 | 2.909 | 3.143 | 5.513 | 50.768 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.078 | 0.043 | 0.113 | 0.914 | 4.340 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.156 | -0.241 | -0.071 | -0.294 | -3.607 | 0.000 |
| d_depre_1 ~~ sa07_11_t1 | -0.063 | -0.130 | 0.004 | -0.156 | -1.847 | 0.065 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.106 | 1.918 | 2.294 | 1.000 | 21.989 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 6422.351 | 6506.327 | 0.942 | 0.053 | 0.052 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.157 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.700 | NA | NA |
| d_depre_1 ~1 | 1.380 | 0.641 | 2.120 | 5.217 | 3.657 | 0.000 |
| depre_t1 ~1 | 4.443 | 4.368 | 4.519 | 10.154 | 115.575 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.052 | 0.011 | 0.093 | 0.738 | 2.471 | 0.013 |
| d_depre_1 ~ depre_t1 | -0.309 | -0.471 | -0.147 | -0.511 | -3.741 | 0.000 |
| d_depre_1 ~~ sa07_11_t1 | -0.008 | -0.069 | 0.053 | -0.024 | -0.259 | 0.795 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.106 | 1.918 | 2.294 | 1.000 | 21.989 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7960.547 | 8044.524 | 0.986 | 0.037 | 0.056 |
# parameters of interest
params_lcs_volat_curr_hyp5 <- broom::tidy(fit_mi_lcs_volat_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.058 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.453 | NA | NA |
| d_volat_1 ~1 | 0.320 | 0.056 | 0.585 | 0.786 | 2.377 | 0.017 |
| volat_t1 ~1 | 3.119 | 2.986 | 3.251 | 3.275 | 45.995 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.147 | 0.064 | 0.230 | 0.885 | 3.457 | 0.001 |
| d_volat_1 ~ volat_t1 | -0.145 | -0.226 | -0.064 | -0.339 | -3.515 | 0.000 |
| d_volat_1 ~~ sa06_01_t1 | 0.015 | -0.051 | 0.081 | 0.044 | 0.444 | 0.657 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7153.372 | 7237.349 | 0.945 | 0.045 | 0.056 |
# parameters of interest
params_lcs_volat_ideal_hyp5 <- broom::tidy(fit_mi_lcs_volat_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.581 | NA | NA |
| d_volat_1 ~1 | 0.294 | -0.052 | 0.641 | 1.135 | 1.663 | 0.096 |
| volat_t1 ~1 | 1.647 | 1.567 | 1.727 | 3.596 | 40.325 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.060 | 0.004 | 0.116 | 0.894 | 2.106 | 0.035 |
| d_volat_1 ~ volat_t1 | -0.185 | -0.410 | 0.040 | -0.326 | -1.608 | 0.108 |
| d_volat_1 ~~ sa06_01_t1 | -0.028 | -0.078 | 0.021 | -0.130 | -1.124 | 0.261 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8215.89 | 8299.867 | 0.941 | 0.078 | 0.111 |
# parameters of interest
params_lcs_volat_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.071 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.467 | NA | NA |
| d_volat_1 ~1 | 0.364 | 0.064 | 0.665 | 0.877 | 2.378 | 0.017 |
| volat_t1 ~1 | 3.119 | 2.986 | 3.251 | 3.276 | 45.998 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.150 | 0.066 | 0.233 | 0.867 | 3.526 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.159 | -0.252 | -0.067 | -0.365 | -3.383 | 0.001 |
| d_volat_1 ~~ sa07_12_t1 | 0.045 | -0.067 | 0.158 | 0.090 | 0.789 | 0.430 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.709 | 1.531 | 1.887 | 1.000 | 18.847 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7410.458 | 7494.434 | 0.929 | 0.052 | 0.064 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.015 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.562 | NA | NA |
| d_volat_1 ~1 | 0.268 | -0.069 | 0.604 | 1.053 | 1.556 | 0.120 |
| volat_t1 ~1 | 1.646 | 1.566 | 1.726 | 3.588 | 40.318 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.059 | 0.002 | 0.115 | 0.908 | 2.041 | 0.041 |
| d_volat_1 ~ volat_t1 | -0.168 | -0.387 | 0.051 | -0.303 | -1.505 | 0.132 |
| d_volat_1 ~~ sa07_12_t1 | -0.012 | -0.076 | 0.051 | -0.039 | -0.383 | 0.701 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.709 | 1.531 | 1.887 | 1.000 | 18.847 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7619.251 | 7703.228 | 0.984 | 0.035 | 0.046 |
# parameters of interest
params_lcs_curio_curr_hyp5 <- broom::tidy(fit_mi_lcs_curio_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.084 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | NA | NA | NA |
| d_curio_1 ~1 | 0.279 | -0.141 | 0.699 | NA | 1.302 | 0.193 |
| curio_t1 ~1 | 4.032 | 3.933 | 4.132 | 7.224 | 79.614 | 0.000 |
| d_curio_1 ~~ d_curio_1 | -0.013 | -0.047 | 0.021 | NA | -0.765 | 0.444 |
| d_curio_1 ~ curio_t1 | -0.055 | -0.156 | 0.046 | NA | -1.059 | 0.289 |
| d_curio_1 ~~ sa06_01_t1 | 0.000 | -0.035 | 0.034 | -0.005 | -0.027 | 0.978 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7555.914 | 7639.89 | 0.967 | 0.037 | 0.054 |
# parameters of interest
params_lcs_curio_ideal_hyp5 <- broom::tidy(fit_mi_lcs_curio_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.076 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.530 | NA | NA |
| d_curio_1 ~1 | 0.860 | 0.157 | 1.563 | 4.220 | 2.398 | 0.016 |
| curio_t1 ~1 | 4.457 | 4.382 | 4.533 | 10.770 | 116.320 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.035 | -0.005 | 0.076 | 0.852 | 1.728 | 0.084 |
| d_curio_1 ~ curio_t1 | -0.189 | -0.343 | -0.035 | -0.384 | -2.407 | 0.016 |
| d_curio_1 ~~ sa06_01_t1 | -0.032 | -0.068 | 0.004 | -0.190 | -1.739 | 0.082 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7877.453 | 7961.429 | 0.951 | 0.062 | 0.086 |
# parameters of interest
params_lcs_curio_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.074 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | NA | NA | NA |
| d_curio_1 ~1 | 0.238 | -0.186 | 0.661 | NA | 1.101 | 0.271 |
| curio_t1 ~1 | 4.033 | 3.933 | 4.132 | 7.264 | 79.624 | 0.000 |
| d_curio_1 ~~ d_curio_1 | -0.014 | -0.048 | 0.020 | NA | -0.805 | 0.421 |
| d_curio_1 ~ curio_t1 | -0.045 | -0.147 | 0.058 | NA | -0.854 | 0.393 |
| d_curio_1 ~~ sa07_13_t1 | 0.023 | -0.032 | 0.077 | 0.146 | 0.819 | 0.413 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.724 | 1.530 | 1.918 | 1.000 | 17.438 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7816.78 | 7900.757 | 0.942 | 0.05 | 0.067 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.090 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.554 | NA | NA |
| d_curio_1 ~1 | 0.947 | 0.214 | 1.679 | 4.540 | 2.532 | 0.011 |
| curio_t1 ~1 | 4.458 | 4.383 | 4.533 | 10.864 | 116.520 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.036 | -0.004 | 0.077 | 0.831 | 1.750 | 0.080 |
| d_curio_1 ~ curio_t1 | -0.209 | -0.370 | -0.048 | -0.411 | -2.539 | 0.011 |
| d_curio_1 ~~ sa07_13_t1 | -0.019 | -0.077 | 0.039 | -0.076 | -0.648 | 0.517 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.724 | 1.530 | 1.918 | 1.000 | 17.438 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7922.328 | 8006.304 | 0.99 | 0.029 | 0.055 |
# parameters of interest
params_lcs_aesth_curr_hyp5 <- broom::tidy(fit_mi_lcs_aesth_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.994 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.376 | NA | NA |
| d_aesth_1 ~1 | 0.188 | -0.081 | 0.457 | 1.466 | 1.373 | 0.170 |
| aesth_t1 ~1 | 2.848 | 2.725 | 2.970 | 8.375 | 45.474 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.016 | -0.005 | 0.037 | 0.970 | 1.491 | 0.136 |
| d_aesth_1 ~ aesth_t1 | -0.066 | -0.162 | 0.030 | -0.174 | -1.343 | 0.179 |
| d_aesth_1 ~~ sa06_01_t1 | 0.004 | -0.018 | 0.026 | 0.038 | 0.383 | 0.701 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7584.606 | 7668.583 | 0.952 | 0.055 | 0.063 |
# parameters of interest
params_lcs_aesth_ideal_hyp5 <- broom::tidy(fit_mi_lcs_aesth_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.964 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.602 | NA | NA |
| d_aesth_1 ~1 | 0.435 | -0.030 | 0.901 | 2.626 | 1.833 | 0.067 |
| aesth_t1 ~1 | 2.903 | 2.767 | 3.038 | 10.931 | 42.046 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.026 | -0.001 | 0.052 | 0.937 | 1.901 | 0.057 |
| d_aesth_1 ~ aesth_t1 | -0.157 | -0.321 | 0.007 | -0.251 | -1.873 | 0.061 |
| d_aesth_1 ~~ sa06_01_t1 | 0.008 | -0.014 | 0.031 | 0.057 | 0.716 | 0.474 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 8226.707 | 8310.683 | 0.967 | 0.055 | 0.087 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.407 | NA | NA |
| d_aesth_1 ~1 | 0.284 | 0.001 | 0.567 | 2.104 | 1.965 | 0.049 |
| aesth_t1 ~1 | 2.848 | 2.725 | 2.970 | 8.421 | 45.470 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.017 | -0.004 | 0.039 | 0.938 | 1.551 | 0.121 |
| d_aesth_1 ~ aesth_t1 | -0.099 | -0.199 | 0.001 | -0.249 | -1.946 | 0.052 |
| d_aesth_1 ~~ sa07_14_t1 | 0.043 | 0.001 | 0.086 | 0.233 | 1.986 | 0.047 |
| sa07_14_t1 ~~ sa07_14_t1 | 2.002 | 1.823 | 2.180 | 1.000 | 21.957 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7892.858 | 7976.835 | 0.948 | 0.058 | 0.068 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.622 | NA | NA |
| d_aesth_1 ~1 | 0.501 | 0.029 | 0.974 | 2.949 | 2.078 | 0.038 |
| aesth_t1 ~1 | 2.903 | 2.767 | 3.038 | 10.862 | 42.043 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.027 | -0.001 | 0.054 | 0.920 | 1.913 | 0.056 |
| d_aesth_1 ~ aesth_t1 | -0.179 | -0.346 | -0.013 | -0.282 | -2.116 | 0.034 |
| d_aesth_1 ~~ sa07_14_t1 | 0.021 | -0.017 | 0.060 | 0.093 | 1.088 | 0.277 |
| sa07_14_t1 ~~ sa07_14_t1 | 2.002 | 1.823 | 2.180 | 1.000 | 21.957 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7625.283 | 7709.259 | 0.956 | 0.059 | 0.05 |
# parameters of interest
params_lcs_imagi_curr_hyp5 <- broom::tidy(fit_mi_lcs_imagi_curr_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.067 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.428 | NA | NA |
| d_imagi_1 ~1 | 0.645 | 0.081 | 1.209 | 2.368 | 2.241 | 0.025 |
| imagi_t1 ~1 | 3.641 | 3.536 | 3.745 | 5.360 | 68.404 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.065 | -0.019 | 0.149 | 0.877 | 1.516 | 0.129 |
| d_imagi_1 ~ imagi_t1 | -0.141 | -0.289 | 0.007 | -0.351 | -1.872 | 0.061 |
| d_imagi_1 ~~ sa06_01_t1 | -0.008 | -0.053 | 0.036 | -0.037 | -0.366 | 0.715 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa06_01_t1 = general acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7122.125 | 7206.101 | 0.925 | 0.061 | 0.06 |
# parameters of interest
params_lcs_imagi_ideal_hyp5 <- broom::tidy(fit_mi_lcs_imagi_ideal_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa06_01_t1", "sa06_01_t1 ~~ sa06_01_t1", "sa06_01_t1 ~1 ", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.640 | NA | NA |
| d_imagi_1 ~1 | 1.012 | 0.148 | 1.875 | 3.102 | 2.297 | 0.022 |
| imagi_t1 ~1 | 4.544 | 4.468 | 4.621 | 8.602 | 115.758 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.092 | 0.030 | 0.155 | 0.867 | 2.901 | 0.004 |
| d_imagi_1 ~ imagi_t1 | -0.225 | -0.410 | -0.040 | -0.365 | -2.386 | 0.017 |
| d_imagi_1 ~~ sa06_01_t1 | -0.038 | -0.082 | 0.007 | -0.139 | -1.664 | 0.096 |
| sa06_01_t1 ~~ sa06_01_t1 | 0.798 | 0.635 | 0.961 | 1.000 | 9.609 | 0.000 |
| sa06_01_t1 ~1 | 4.283 | 4.187 | 4.378 | 4.794 | 87.878 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7952.997 | 8036.974 | 0.876 | 0.104 | 0.121 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp5 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.099 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.472 | NA | NA |
| d_imagi_1 ~1 | 0.781 | 0.203 | 1.359 | 2.679 | 2.650 | 0.008 |
| imagi_t1 ~1 | 3.641 | 3.536 | 3.745 | 5.361 | 68.422 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.070 | -0.013 | 0.153 | 0.827 | 1.661 | 0.097 |
| d_imagi_1 ~ imagi_t1 | -0.178 | -0.331 | -0.026 | -0.416 | -2.293 | 0.022 |
| d_imagi_1 ~~ sa07_15_t1 | -0.078 | -0.167 | 0.012 | -0.200 | -1.696 | 0.090 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.135 | 1.956 | 2.314 | 1.000 | 23.329 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp5) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 22 | 7451.466 | 7535.443 | 0.899 | 0.072 | 0.07 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp5 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp5, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp5, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.055 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.673 | NA | NA |
| d_imagi_1 ~1 | 1.143 | 0.275 | 2.011 | 3.392 | 2.582 | 0.010 |
| imagi_t1 ~1 | 4.545 | 4.468 | 4.622 | 8.593 | 115.680 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.096 | 0.033 | 0.158 | 0.841 | 2.992 | 0.003 |
| d_imagi_1 ~ imagi_t1 | -0.254 | -0.441 | -0.068 | -0.399 | -2.671 | 0.008 |
| d_imagi_1 ~~ sa07_15_t1 | -0.078 | -0.163 | 0.007 | -0.174 | -1.809 | 0.070 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.135 | 1.956 | 2.314 | 1.000 | 23.329 | 0.000 |
Run models for all facets with a template & loop:
# create df for table:
df_table_hyp5 <- bind_rows(
#traits
params_lcs_extra_curr_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_ideal_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_curr_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_ideal_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_curr_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_ideal_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_curr_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_ideal_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_curr_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_ideal_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_curr_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_ideal_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_curr_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_socia_ideal_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_asser_curr_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_ideal_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_curr_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_asser_ideal_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_energ_curr_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_ideal_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_curr_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_energ_ideal_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_compa_curr_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_ideal_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_curr_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_compa_ideal_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_respe_curr_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_ideal_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_curr_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_respe_ideal_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_trust_curr_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_ideal_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_curr_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_trust_ideal_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_organ_curr_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_ideal_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_curr_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_organ_ideal_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_produ_curr_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_ideal_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_curr_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_produ_ideal_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_respo_curr_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_ideal_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_curr_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_respo_ideal_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_anxie_curr_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_ideal_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_curr_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_anxie_ideal_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_depre_curr_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_ideal_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_curr_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_depre_ideal_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_volat_curr_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_ideal_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_curr_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_volat_ideal_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_curio_curr_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_ideal_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_curr_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_curio_ideal_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_aesth_curr_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_ideal_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_curr_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_aesth_ideal_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_imagi_curr_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_ideal_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_curr_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
params_lcs_imagi_ideal_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, std.all, statistic, p.value)Results summary across the Big Five traits: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[1:20, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extraversion | current | general | 0.008 | 0.037 | 0.446 | 0.656 |
| extraversion | ideal | general | 0.016 | 0.073 | 0.778 | 0.437 |
| extraversion | current | specific | -0.035 | -0.155 | -1.181 | 0.238 |
| extraversion | ideal | specific | -0.011 | -0.050 | -0.443 | 0.658 |
| agreeableness | current | general | -0.008 | -0.043 | -0.605 | 0.545 |
| agreeableness | ideal | general | 0.000 | -0.002 | -0.020 | 0.984 |
| agreeableness | current | specific | -0.059 | -0.309 | -2.392 | 0.017 |
| agreeableness | ideal | specific | -0.024 | -0.100 | -1.087 | 0.277 |
| conscientiousness | current | general | -0.031 | -0.104 | -1.418 | 0.156 |
| conscientiousness | ideal | general | 0.017 | 0.065 | 1.044 | 0.297 |
| conscientiousness | current | specific | -0.062 | -0.167 | -1.707 | 0.088 |
| conscientiousness | ideal | specific | -0.050 | -0.162 | -2.036 | 0.042 |
| neuroticism | current | general | -0.023 | -0.071 | -1.082 | 0.279 |
| neuroticism | ideal | general | -0.016 | -0.056 | -0.681 | 0.496 |
| neuroticism | current | specific | 0.066 | 0.186 | 2.121 | 0.034 |
| neuroticism | ideal | specific | 0.003 | 0.009 | 0.116 | 0.908 |
| openness | current | general | -0.014 | -0.067 | -0.794 | 0.427 |
| openness | ideal | general | -0.025 | -0.143 | -1.941 | 0.052 |
| openness | current | specific | -0.024 | -0.133 | -1.099 | 0.272 |
| openness | ideal | specific | -0.024 | -0.157 | -2.101 | 0.036 |
Results summary across the Big Five facets: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[21:80, ], digits = 3)| trait | ref | goal | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| sociability | current | general | 0.032 | 0.090 | 0.879 | 0.379 |
| sociability | ideal | general | -0.020 | -0.118 | -0.742 | 0.458 |
| sociability | current | specific | -0.066 | -0.119 | -1.284 | 0.199 |
| sociability | ideal | specific | -0.059 | -0.239 | -1.742 | 0.082 |
| assertiveness | current | general | -0.009 | -0.041 | -0.457 | 0.648 |
| assertiveness | ideal | general | 0.035 | 0.240 | 1.826 | 0.068 |
| assertiveness | current | specific | -0.006 | -0.018 | -0.164 | 0.870 |
| assertiveness | ideal | specific | 0.060 | 0.273 | 1.817 | 0.069 |
| energy | current | general | 0.003 | 0.019 | 0.245 | 0.806 |
| energy | ideal | general | -0.009 | -0.067 | -0.781 | 0.435 |
| energy | current | specific | 0.038 | 0.155 | 1.415 | 0.157 |
| energy | ideal | specific | -0.007 | -0.035 | -0.398 | 0.690 |
| compassion | current | general | -0.039 | -0.112 | -1.236 | 0.216 |
| compassion | ideal | general | -0.005 | -0.015 | -0.138 | 0.890 |
| compassion | current | specific | -0.131 | -0.230 | -2.053 | 0.040 |
| compassion | ideal | specific | -0.060 | -0.119 | -1.036 | 0.300 |
| respectfulness | current | general | -0.007 | -0.023 | -0.308 | 0.758 |
| respectfulness | ideal | general | -0.004 | -0.030 | -0.209 | 0.834 |
| respectfulness | current | specific | -0.195 | -0.446 | -3.818 | 0.000 |
| respectfulness | ideal | specific | -0.030 | -0.156 | -0.984 | 0.325 |
| trust | current | general | -0.007 | -0.040 | -0.283 | 0.777 |
| trust | ideal | general | -0.008 | -0.038 | -0.371 | 0.710 |
| trust | current | specific | -0.055 | -0.198 | -1.108 | 0.268 |
| trust | ideal | specific | -0.025 | -0.073 | -0.629 | 0.529 |
| organization | current | general | 0.074 | 0.185 | 2.143 | 0.032 |
| organization | ideal | general | 0.003 | 0.013 | 0.139 | 0.889 |
| organization | current | specific | 0.213 | 0.311 | 3.323 | 0.001 |
| organization | ideal | specific | 0.051 | 0.139 | 1.259 | 0.208 |
| productiveness | current | general | 0.031 | 0.092 | 1.111 | 0.267 |
| productiveness | ideal | general | -0.006 | -0.029 | -0.374 | 0.708 |
| productiveness | current | specific | 0.100 | 0.186 | 1.833 | 0.067 |
| productiveness | ideal | specific | 0.050 | 0.166 | 1.789 | 0.074 |
| responsibility | current | general | -0.009 | -0.060 | -0.668 | 0.504 |
| responsibility | ideal | general | 0.035 | 0.147 | 1.685 | 0.092 |
| responsibility | current | specific | -0.048 | -0.185 | -1.505 | 0.132 |
| responsibility | ideal | specific | -0.059 | -0.155 | -1.689 | 0.091 |
| anxiety | current | general | 0.027 | 0.075 | 0.778 | 0.437 |
| anxiety | ideal | general | 0.012 | 0.065 | 0.469 | 0.639 |
| anxiety | current | specific | -0.165 | -0.269 | -2.652 | 0.008 |
| anxiety | ideal | specific | -0.013 | -0.042 | -0.387 | 0.698 |
| depression | current | general | 0.006 | 0.025 | 0.327 | 0.744 |
| depression | ideal | general | 0.002 | 0.011 | 0.106 | 0.916 |
| depression | current | specific | -0.063 | -0.156 | -1.847 | 0.065 |
| depression | ideal | specific | -0.008 | -0.024 | -0.259 | 0.795 |
| volatility | current | general | 0.015 | 0.044 | 0.444 | 0.657 |
| volatility | ideal | general | -0.028 | -0.130 | -1.124 | 0.261 |
| volatility | current | specific | 0.045 | 0.090 | 0.789 | 0.430 |
| volatility | ideal | specific | -0.012 | -0.039 | -0.383 | 0.701 |
| curiosity | current | general | 0.000 | -0.005 | -0.027 | 0.978 |
| curiosity | ideal | general | -0.032 | -0.190 | -1.739 | 0.082 |
| curiosity | current | specific | 0.023 | 0.146 | 0.819 | 0.413 |
| curiosity | ideal | specific | -0.019 | -0.076 | -0.648 | 0.517 |
| aesthetic | current | general | 0.004 | 0.038 | 0.383 | 0.701 |
| aesthetic | ideal | general | 0.008 | 0.057 | 0.716 | 0.474 |
| aesthetic | current | specific | 0.043 | 0.233 | 1.986 | 0.047 |
| aesthetic | ideal | specific | 0.021 | 0.093 | 1.088 | 0.277 |
| imagination | current | general | -0.008 | -0.037 | -0.366 | 0.715 |
| imagination | ideal | general | -0.038 | -0.139 | -1.664 | 0.096 |
| imagination | current | specific | -0.078 | -0.200 | -1.696 | 0.090 |
| imagination | ideal | specific | -0.078 | -0.174 | -1.809 | 0.070 |
Prepare data frame for plotting:
df_table_hyp5_plot <- bind_rows(
#traits
params_lcs_extra_curr_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_ideal_hyp5 %>% filter(term=="d_extra_1 ~~ sa06_01_t1"),
params_lcs_extra_curr_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_extra_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_extra_1"),
params_lcs_agree_curr_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_ideal_hyp5 %>% filter(term=="d_agree_1 ~~ sa06_01_t1"),
params_lcs_agree_curr_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_agree_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_agree_1"),
params_lcs_consc_curr_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_ideal_hyp5 %>% filter(term=="d_consc_1 ~~ sa06_01_t1"),
params_lcs_consc_curr_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_consc_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_consc_1"),
params_lcs_neuro_curr_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_ideal_hyp5 %>% filter(term=="d_neuro_1 ~~ sa06_01_t1"),
params_lcs_neuro_curr_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_neuro_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_neuro_1"),
params_lcs_openn_curr_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_ideal_hyp5 %>% filter(term=="d_openn_1 ~~ sa06_01_t1"),
params_lcs_openn_curr_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
params_lcs_openn_ideal_specif_hyp5 %>% filter(term=="goals ~~ d_openn_1"),
#facets
params_lcs_socia_curr_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_ideal_hyp5 %>% filter(term=="d_socia_1 ~~ sa06_01_t1"),
params_lcs_socia_curr_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_socia_ideal_specif_hyp5 %>% filter(term=="d_socia_1 ~~ sa07_01_t1"),
params_lcs_asser_curr_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_ideal_hyp5 %>% filter(term=="d_asser_1 ~~ sa06_01_t1"),
params_lcs_asser_curr_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_asser_ideal_specif_hyp5 %>% filter(term=="d_asser_1 ~~ sa07_02_t1"),
params_lcs_energ_curr_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_ideal_hyp5 %>% filter(term=="d_energ_1 ~~ sa06_01_t1"),
params_lcs_energ_curr_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_energ_ideal_specif_hyp5 %>% filter(term=="d_energ_1 ~~ sa07_03_t1"),
params_lcs_compa_curr_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_ideal_hyp5 %>% filter(term=="d_compa_1 ~~ sa06_01_t1"),
params_lcs_compa_curr_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_compa_ideal_specif_hyp5 %>% filter(term=="d_compa_1 ~~ sa07_04_t1"),
params_lcs_respe_curr_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_ideal_hyp5 %>% filter(term=="d_respe_1 ~~ sa06_01_t1"),
params_lcs_respe_curr_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_respe_ideal_specif_hyp5 %>% filter(term=="d_respe_1 ~~ sa07_05_t1"),
params_lcs_trust_curr_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_ideal_hyp5 %>% filter(term=="d_trust_1 ~~ sa06_01_t1"),
params_lcs_trust_curr_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_trust_ideal_specif_hyp5 %>% filter(term=="d_trust_1 ~~ sa07_06_t1"),
params_lcs_organ_curr_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_ideal_hyp5 %>% filter(term=="d_organ_1 ~~ sa06_01_t1"),
params_lcs_organ_curr_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_organ_ideal_specif_hyp5 %>% filter(term=="d_organ_1 ~~ sa07_07_t1"),
params_lcs_produ_curr_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_ideal_hyp5 %>% filter(term=="d_produ_1 ~~ sa06_01_t1"),
params_lcs_produ_curr_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_produ_ideal_specif_hyp5 %>% filter(term=="d_produ_1 ~~ sa07_08_t1"),
params_lcs_respo_curr_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_ideal_hyp5 %>% filter(term=="d_respo_1 ~~ sa06_01_t1"),
params_lcs_respo_curr_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_respo_ideal_specif_hyp5 %>% filter(term=="d_respo_1 ~~ sa07_09_t1"),
params_lcs_anxie_curr_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_ideal_hyp5 %>% filter(term=="d_anxie_1 ~~ sa06_01_t1"),
params_lcs_anxie_curr_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_anxie_ideal_specif_hyp5 %>% filter(term=="d_anxie_1 ~~ sa07_10_t1"),
params_lcs_depre_curr_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_ideal_hyp5 %>% filter(term=="d_depre_1 ~~ sa06_01_t1"),
params_lcs_depre_curr_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_depre_ideal_specif_hyp5 %>% filter(term=="d_depre_1 ~~ sa07_11_t1"),
params_lcs_volat_curr_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_ideal_hyp5 %>% filter(term=="d_volat_1 ~~ sa06_01_t1"),
params_lcs_volat_curr_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_volat_ideal_specif_hyp5 %>% filter(term=="d_volat_1 ~~ sa07_12_t1"),
params_lcs_curio_curr_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_ideal_hyp5 %>% filter(term=="d_curio_1 ~~ sa06_01_t1"),
params_lcs_curio_curr_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_curio_ideal_specif_hyp5 %>% filter(term=="d_curio_1 ~~ sa07_13_t1"),
params_lcs_aesth_curr_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_ideal_hyp5 %>% filter(term=="d_aesth_1 ~~ sa06_01_t1"),
params_lcs_aesth_curr_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_aesth_ideal_specif_hyp5 %>% filter(term=="d_aesth_1 ~~ sa07_14_t1"),
params_lcs_imagi_curr_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_ideal_hyp5 %>% filter(term=="d_imagi_1 ~~ sa06_01_t1"),
params_lcs_imagi_curr_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
params_lcs_imagi_ideal_specif_hyp5 %>% filter(term=="d_imagi_1 ~~ sa07_15_t1"),
) %>%
mutate(trait = rep(names(b5_vars), each=4),
ref = rep(rep(c("current", "ideal"), 2), 20),
goal = rep(c(rep("general", 2), rep("specific", 2)), 20)) %>%
select(trait, ref, goal, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp5_plot <- df_table_hyp5_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor(ref, levels = c("current", "ideal"), labels = c("current", "ideal"))) %>%
mutate(goal = factor(goal, levels = c("general", "specific"), labels = c("general", "specific"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp5_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + goal, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
In the self-improvement group, change in current-self ratings but not change in ideal-self ratings will be moderated by change goals (both general and trait-/facet-specific goals) and the frequency of self-improvement behaviors.
We will test this one domain/facet at a time. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and specify change goals (or the frequency of self-improvement behaviors) as a moderator of latent trait change. We will check in a second set of models whether changes in current-self ratings persist from T2 to T3.
Reshape and split data set by intervention group:
# from T1 to T2
df_sbsa2_wide_pers_sb_mod <- df_sbsa2 %>%
filter(rando=="Self-Improvement" & time %in% c(1,2)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07", # facet-specific change goals
"sb04"))) %>% # frequency self-improvement behaviors
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sb07", "sb04")))) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2,
sb04_01_t1, sb04_02_t1, sb04_03_t1)) # frequency of self-improvement behaviors measured at T2
# colnames(df_sbsa2_wide_pers_sb_mod)
group_assign <- df_sbsa2 %>% select(pid, rando) %>% unique()
df_sbsa2_wide_pers_sb_mod <- df_sbsa2_wide_pers %>% left_join(group_assign) %>%
filter(rando=="Self-Improvement") %>% select(-rando, -ends_with("_t3")) %>%
left_join(df_sbsa2_wide_pers_sb_mod)
# follow-up, from T2 to T3
df_sbsa2_wide_pers_sb_mod_fu <- df_sbsa2 %>%
filter(rando=="Self-Improvement" & time %in% c(1,2)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sb07", # facet-specific change goals -> asked at T1
"sb04"))) %>% # frequency self-improvement behaviors -> asked at T2
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sb07", "sb04")))) %>%
select(-c(sb07_01_t2, sb07_02_t2, sb07_03_t2, sb07_04_t2, sb07_05_t2,
sb07_06_t2, sb07_07_t2, sb07_08_t2, sb07_09_t2, sb07_10_t2,
sb07_11_t2, sb07_12_t2, sb07_13_t2, sb07_14_t2, sb07_15_t2,
sb04_01_t1, sb04_02_t1, sb04_03_t1)) # frequency of self-improvement behaviors measured at T2
# colnames(df_sbsa2_wide_pers_sb_mod_fu)
df_sbsa2_wide_pers_sb_mod_fu <- df_sbsa2_wide_pers %>% left_join(group_assign) %>%
filter(rando=="Self-Improvement" & !is.na(valid_t2)) %>% select(-rando, -ends_with("_t1")) %>%
left_join(df_sbsa2_wide_pers_sb_mod_fu)Run models for all traits with templates & loops:
# create templates:
# 1st, for facet-specific change goals
trait_template_mod_goal <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ goals # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
trait_facets_nrs <- list(a1 = c(1:3), b2 = c(4:6), c3 = c(7:9), d4 = c(10:12), e5 = c(13:15)) # matching facet nrs to traits
# loop across 5 traits -> change in current-self ratings
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
mod_names = paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2],
"ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), trait_model_fit))
}
# loop across 5 traits -> change in *ideal*-self ratings (not hypothesis-conform)
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
mod_names = paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"), # ideal instead of current!
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2],
"ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp6")), template_filled)) # ideal instead of curr
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp6")), trait_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for current self ratings)
trait_template_mod_goal_fu <- '
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2
trait_t3 =~ 1*ind01_t3 + lamb2*ind02_t3 + lamb3*ind03_t3 # This specifies the measurement model for trait_t3 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t3 ~ 1*trait_t2 # This parameter regresses trait_t3 perfectly on trait_t2
d_trait_1 =~ 1*trait_t3 # This defines the latent change score factor as measured perfectly by scores on trait_t3
trait_t3 ~ 0*1 # This line constrains the intercept of trait_t3 to 0
trait_t3 ~~ 0*trait_t3 # This fixes the variance of trait_t3 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t2 ~ 1 # This estimates the intercept of trait_t2
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t2 ~~ trait_t2 # This estimates the variance of trait_t2
trait_t2 ~ goals # This estimates the moderation effect on personality at T2
d_trait_1 ~ trait_t2 + goals # This estimates the self-feedback parameter and the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t2 ~~ ind01_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind02_t2 ~~ ind02_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind03_t2 ~~ ind03_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t3 ~~ res1*ind01_t3 # This allows residual variance on indicator X1 at T3
ind02_t3 ~~ res2*ind02_t3 # This allows residual variance on indicator X2 at T3
ind03_t3 ~~ res3*ind03_t3 # This allows residual variance on indicator X3 at T3
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind01_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind02_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind03_t3 ~ m3*1 # This estimates the intercept of X3 at T3
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
# loop across 5 traits -> change in current-self ratings
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
mod_names = paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal_fu,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2],
"ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6_fu")), trait_model_fit))
}
# 2nd, for frequency of self-improvement behavior
trait_template_mod_frequ <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), trait_model_fit))
}
# loop across 5 traits -> change in *ideal*-self ratings (not hypothesis-conform)
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"), # ideal instead of current!
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp6")), template_filled)) # ideal instead of curr
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp6")), trait_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for current self ratings)
trait_template_mod_frequ_fu <- '
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2
trait_t3 =~ 1*ind01_t3 + lamb2*ind02_t3 + lamb3*ind03_t3 # This specifies the measurement model for extra_t3 with the equality constrained factor loadings
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
trait_t3 ~ 1*trait_t2 # This parameter regresses trait_t3 perfectly on trait_t2
d_trait_1 =~ 1*trait_t3 # This defines the latent change score factor as measured perfectly by scores on trait_t3
trait_t3 ~ 0*1 # This line constrains the intercept of trait_t3 to 0
trait_t3 ~~ 0*trait_t3 # This fixes the variance of trait_t3 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t2 ~ 1 # This estimates the intercept of trait_t2
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t2 ~~ trait_t2 # This estimates the variance of trait_t2
trait_t2 ~ frequ # This estimates the moderation effect on personality at T2
d_trait_1 ~ trait_t2 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t2 ~~ ind01_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind02_t2 ~~ ind02_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind03_t2 ~~ ind03_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t3 ~~ res1*ind01_t3 # This allows residual variance on indicator X1 at T3
ind02_t3 ~~ res2*ind02_t3 # This allows residual variance on indicator X2 at T3
ind03_t3 ~~ res3*ind03_t3 # This allows residual variance on indicator X3 at T3
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind01_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind02_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind03_t3 ~ m3*1 # This estimates the intercept of X3 at T3
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 5 traits
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ_fu,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"),
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6_fu")), trait_model_fit))
} Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6891.241 | 6990.64 | 0.941 | 0.088 | 0.049 |
# parameters of interest
params_lcs_extra_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.080 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.525 | NA | NA |
| d_extra_1 ~1 | 0.906 | 0.316 | 1.497 | 3.114 | 3.009 | 0.003 |
| extra_t1 ~1 | 2.987 | 2.901 | 3.073 | 4.990 | 67.877 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.070 | 0.035 | 0.105 | 0.823 | 3.930 | 0.000 |
| extra_t1 ~ goals | -0.579 | -0.805 | -0.353 | -0.725 | -5.019 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.271 | -0.469 | -0.073 | -0.557 | -2.682 | 0.007 |
| d_extra_1 ~ goals | -0.090 | -0.267 | 0.087 | -0.231 | -0.994 | 0.320 |
| goals ~~ goals | 0.562 | 0.296 | 0.828 | 1.000 | 4.145 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5919.022 | 6018.422 | 0.972 | 0.062 | 0.043 |
# parameters of interest
params_lcs_extra_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_extra_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.080 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.524 | NA | NA |
| d_extra_1 ~1 | 0.683 | 0.425 | 0.941 | 2.353 | 5.187 | 0.000 |
| extra_t1 ~1 | 2.987 | 2.901 | 3.073 | 5.000 | 67.984 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.065 | 0.032 | 0.097 | 0.767 | 3.912 | 0.000 |
| extra_t1 ~ frequ | 0.049 | -0.098 | 0.195 | 0.051 | 0.651 | 0.515 |
| d_extra_1 ~ extra_t1 | -0.196 | -0.281 | -0.112 | -0.404 | -4.545 | 0.000 |
| d_extra_1 ~ frequ | 0.132 | 0.049 | 0.216 | 0.285 | 3.117 | 0.002 |
| frequ ~~ frequ | 0.391 | 0.253 | 0.529 | 1.000 | 5.553 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6241.705 | 6341.104 | 1 | 0 | 0.033 |
# parameters of interest
params_lcs_agree_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.074 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.535 | NA | NA |
| d_agree_1 ~1 | 0.672 | 0.271 | 1.073 | 3.306 | 3.282 | 0.001 |
| agree_t1 ~1 | 3.463 | 3.395 | 3.531 | 8.489 | 99.804 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.035 | 0.015 | 0.055 | 0.854 | 3.430 | 0.001 |
| agree_t1 ~ goals | -0.146 | -0.219 | -0.073 | -0.330 | -3.911 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.188 | -0.304 | -0.072 | -0.378 | -3.186 | 0.001 |
| d_agree_1 ~ goals | 0.003 | -0.045 | 0.052 | 0.015 | 0.132 | 0.895 |
| goals ~~ goals | 0.852 | 0.558 | 1.146 | 1.000 | 5.688 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5242.916 | 5342.316 | 0.989 | 0.033 | 0.041 |
# parameters of interest
params_lcs_agree_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_agree_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.086 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_agree_1 ~1 | 0.810 | 0.416 | 1.204 | 3.773 | 4.034 | 0.000 |
| agree_t1 ~1 | 3.462 | 3.394 | 3.530 | 8.023 | 99.324 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.037 | 0.015 | 0.059 | 0.809 | 3.353 | 0.001 |
| agree_t1 ~ frequ | 0.209 | 0.084 | 0.334 | 0.307 | 3.264 | 0.001 |
| d_agree_1 ~ agree_t1 | -0.228 | -0.340 | -0.115 | -0.458 | -3.971 | 0.000 |
| d_agree_1 ~ frequ | 0.061 | -0.008 | 0.130 | 0.181 | 1.735 | 0.083 |
| frequ ~~ frequ | 0.402 | 0.264 | 0.540 | 1.000 | 5.709 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6354.925 | 6454.324 | 0.989 | 0.05 | 0.031 |
# parameters of interest
params_lcs_consc_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.533 | NA | NA |
| d_consc_1 ~1 | 0.789 | 0.314 | 1.264 | 1.902 | 3.257 | 0.001 |
| consc_t1 ~1 | 3.378 | 3.287 | 3.469 | 4.353 | 72.745 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.158 | 0.083 | 0.232 | 0.917 | 4.151 | 0.000 |
| consc_t1 ~ goals | -0.447 | -0.515 | -0.380 | -0.693 | -12.973 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.202 | -0.344 | -0.060 | -0.378 | -2.795 | 0.005 |
| d_consc_1 ~ goals | -0.058 | -0.138 | 0.021 | -0.169 | -1.444 | 0.149 |
| goals ~~ goals | 1.445 | 1.175 | 1.715 | 1.000 | 10.490 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5522.336 | 5621.736 | 0.992 | 0.038 | 0.031 |
# parameters of interest
params_lcs_consc_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_consc_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.533 | NA | NA |
| d_consc_1 ~1 | 0.593 | 0.285 | 0.900 | 1.443 | 3.773 | 0.000 |
| consc_t1 ~1 | 3.378 | 3.287 | 3.469 | 4.405 | 72.829 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.155 | 0.079 | 0.232 | 0.923 | 4.000 | 0.000 |
| consc_t1 ~ frequ | 0.136 | -0.040 | 0.313 | 0.111 | 1.511 | 0.131 |
| d_consc_1 ~ consc_t1 | -0.145 | -0.234 | -0.056 | -0.271 | -3.182 | 0.001 |
| d_consc_1 ~ frequ | 0.067 | -0.039 | 0.173 | 0.102 | 1.233 | 0.217 |
| frequ ~~ frequ | 0.393 | 0.253 | 0.532 | 1.000 | 5.525 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6722.804 | 6822.203 | 0.99 | 0.047 | 0.029 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.070 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.586 | NA | NA |
| d_neuro_1 ~1 | 0.806 | 0.408 | 1.203 | 1.747 | 3.974 | 0.000 |
| neuro_t1 ~1 | 3.276 | 3.179 | 3.372 | 3.890 | 66.638 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.175 | 0.133 | 0.218 | 0.824 | 8.135 | 0.000 |
| neuro_t1 ~ goals | 0.532 | 0.443 | 0.622 | 0.718 | 11.663 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.301 | -0.422 | -0.179 | -0.549 | -4.854 | 0.000 |
| d_neuro_1 ~ goals | 0.090 | -0.001 | 0.181 | 0.222 | 1.931 | 0.053 |
| goals ~~ goals | 1.291 | 0.989 | 1.592 | 1.000 | 8.394 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5761.746 | 5861.145 | 0.992 | 0.04 | 0.038 |
# parameters of interest
params_lcs_neuro_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_neuro_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.071 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.588 | NA | NA |
| d_neuro_1 ~1 | 0.528 | 0.286 | 0.770 | 1.145 | 4.272 | 0.000 |
| neuro_t1 ~1 | 3.276 | 3.179 | 3.372 | 3.897 | 66.619 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.179 | 0.135 | 0.223 | 0.842 | 7.945 | 0.000 |
| neuro_t1 ~ frequ | -0.050 | -0.243 | 0.144 | -0.037 | -0.503 | 0.615 |
| d_neuro_1 ~ neuro_t1 | -0.216 | -0.292 | -0.140 | -0.394 | -5.553 | 0.000 |
| d_neuro_1 ~ frequ | -0.048 | -0.155 | 0.058 | -0.066 | -0.886 | 0.376 |
| frequ ~~ frequ | 0.393 | 0.254 | 0.533 | 1.000 | 5.513 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6284.378 | 6383.777 | 0.983 | 0.052 | 0.037 |
# parameters of interest
params_lcs_openn_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.455 | NA | NA |
| d_openn_1 ~1 | 0.461 | 0.096 | 0.827 | 1.717 | 2.473 | 0.013 |
| openn_t1 ~1 | 3.485 | 3.409 | 3.560 | 5.931 | 90.550 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.068 | 0.038 | 0.099 | 0.949 | 4.386 | 0.000 |
| openn_t1 ~ goals | -0.248 | -0.356 | -0.141 | -0.335 | -4.525 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.109 | -0.212 | -0.007 | -0.239 | -2.090 | 0.037 |
| d_openn_1 ~ goals | -0.020 | -0.088 | 0.048 | -0.060 | -0.586 | 0.558 |
| goals ~~ goals | 0.628 | 0.411 | 0.845 | 1.000 | 5.672 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5464.696 | 5564.095 | 0.997 | 0.019 | 0.038 |
# parameters of interest
params_lcs_openn_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_openn_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.456 | NA | NA |
| d_openn_1 ~1 | 0.495 | 0.145 | 0.845 | 1.870 | 2.770 | 0.006 |
| openn_t1 ~1 | 3.486 | 3.410 | 3.561 | 6.022 | 90.667 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.065 | 0.035 | 0.094 | 0.924 | 4.348 | 0.000 |
| openn_t1 ~ frequ | 0.221 | 0.081 | 0.361 | 0.238 | 3.095 | 0.002 |
| d_openn_1 ~ openn_t1 | -0.120 | -0.217 | -0.022 | -0.262 | -2.408 | 0.016 |
| d_openn_1 ~ frequ | 0.072 | -0.016 | 0.159 | 0.169 | 1.605 | 0.108 |
| frequ ~~ frequ | 0.388 | 0.250 | 0.526 | 1.000 | 5.512 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5894.919 | 5990.604 | 0.984 | 0.046 | 0.04 |
# parameters of interest
params_lcs_extra_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t3 ~ extra_t2", "d_extra_1 =~ extra_t3", "d_extra_1 ~ extra_t2", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t2 ~ goals", # change goals
"d_extra_1 ~1 ", "extra_t2 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t3 ~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.974 | NA | NA |
| d_extra_1 =~ extra_t3 | 1.000 | 1.000 | 1.000 | 0.553 | NA | NA |
| d_extra_1 ~1 | 0.240 | -0.466 | 0.946 | 0.784 | 0.666 | 0.505 |
| extra_t2 ~1 | 3.141 | 3.052 | 3.229 | 5.832 | 69.404 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.087 | 0.033 | 0.141 | 0.930 | 3.167 | 0.002 |
| extra_t2 ~ goals | -0.440 | -0.600 | -0.280 | -0.678 | -5.385 | 0.000 |
| d_extra_1 ~ extra_t2 | -0.070 | -0.298 | 0.157 | -0.124 | -0.606 | 0.545 |
| d_extra_1 ~ goals | 0.061 | -0.088 | 0.210 | 0.165 | 0.798 | 0.425 |
| goals ~~ goals | 0.690 | 0.370 | 1.011 | 1.000 | 4.222 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5342.784 | 5438.468 | 0.988 | 0.042 | 0.045 |
# parameters of interest
params_lcs_extra_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_extra_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t3 ~ extra_t2", "d_extra_1 =~ extra_t3", "d_extra_1 ~ extra_t2", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t2 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t2 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t3 ~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_extra_1 =~ extra_t3 | 1.000 | 1.000 | 1.000 | 0.556 | NA | NA |
| d_extra_1 ~1 | 0.461 | 0.103 | 0.818 | 1.461 | 2.525 | 0.012 |
| extra_t2 ~1 | 3.141 | 3.052 | 3.230 | 5.660 | 69.146 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.093 | 0.039 | 0.148 | 0.940 | 3.354 | 0.001 |
| extra_t2 ~ frequ | 0.186 | 0.032 | 0.341 | 0.208 | 2.366 | 0.018 |
| d_extra_1 ~ extra_t2 | -0.141 | -0.256 | -0.026 | -0.248 | -2.399 | 0.016 |
| d_extra_1 ~ frequ | 0.010 | -0.085 | 0.104 | 0.019 | 0.201 | 0.840 |
| frequ ~~ frequ | 0.384 | 0.246 | 0.523 | 1.000 | 5.434 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5370.967 | 5466.651 | 0.988 | 0.035 | 0.038 |
# parameters of interest
params_lcs_agree_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t3 ~ agree_t2", "d_agree_1 =~ agree_t3", "d_agree_1 ~ agree_t2", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t2 ~ goals", # change goals
"d_agree_1 ~1 ", "agree_t2 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t3 ~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_agree_1 =~ agree_t3 | 1.000 | 1.000 | 1.000 | 0.370 | NA | NA |
| d_agree_1 ~1 | 0.198 | -0.218 | 0.614 | 1.450 | 0.933 | 0.351 |
| agree_t2 ~1 | 3.472 | 3.403 | 3.542 | 9.551 | 98.183 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.018 | -0.003 | 0.039 | 0.977 | 1.688 | 0.091 |
| agree_t2 ~ goals | -0.105 | -0.171 | -0.039 | -0.262 | -3.118 | 0.002 |
| d_agree_1 ~ agree_t2 | -0.059 | -0.177 | 0.059 | -0.157 | -0.980 | 0.327 |
| d_agree_1 ~ goals | -0.009 | -0.057 | 0.038 | -0.062 | -0.387 | 0.699 |
| goals ~~ goals | 0.827 | 0.490 | 1.165 | 1.000 | 4.805 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 4780.544 | 4876.228 | 0.99 | 0.032 | 0.043 |
# parameters of interest
params_lcs_agree_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_agree_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t3 ~ agree_t2", "d_agree_1 =~ agree_t3", "d_agree_1 ~ agree_t2", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t2 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t2 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t3 ~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.982 | NA | NA |
| d_agree_1 =~ agree_t3 | 1.000 | 1.000 | 1.000 | 0.360 | NA | NA |
| d_agree_1 ~1 | 0.054 | -0.435 | 0.543 | 0.377 | 0.216 | 0.829 |
| agree_t2 ~1 | 3.473 | 3.403 | 3.542 | 8.888 | 97.530 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.019 | -0.006 | 0.044 | 0.937 | 1.493 | 0.135 |
| agree_t2 ~ frequ | 0.222 | 0.105 | 0.339 | 0.362 | 3.708 | 0.000 |
| d_agree_1 ~ agree_t2 | -0.018 | -0.155 | 0.120 | -0.049 | -0.255 | 0.799 |
| d_agree_1 ~ frequ | -0.051 | -0.123 | 0.020 | -0.229 | -1.406 | 0.160 |
| frequ ~~ frequ | 0.408 | 0.269 | 0.546 | 1.000 | 5.757 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5580.107 | 5675.792 | 0.995 | 0.032 | 0.034 |
# parameters of interest
params_lcs_consc_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t3 ~ consc_t2", "d_consc_1 =~ consc_t3", "d_consc_1 ~ consc_t2", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t2 ~ goals", # change goals
"d_consc_1 ~1 ", "consc_t2 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t3 ~ consc_t2 | 1.000 | 1.000 | 1.000 | 1.043 | NA | NA |
| d_consc_1 =~ consc_t3 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_consc_1 ~1 | 0.895 | 0.312 | 1.478 | 2.068 | 3.008 | 0.003 |
| consc_t2 ~1 | 3.507 | 3.412 | 3.602 | 4.615 | 72.539 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.162 | 0.084 | 0.239 | 0.862 | 4.102 | 0.000 |
| consc_t2 ~ goals | -0.406 | -0.477 | -0.334 | -0.627 | -11.135 | 0.000 |
| d_consc_1 ~ consc_t2 | -0.252 | -0.416 | -0.088 | -0.443 | -3.017 | 0.003 |
| d_consc_1 ~ goals | -0.051 | -0.147 | 0.046 | -0.138 | -1.034 | 0.301 |
| goals ~~ goals | 1.381 | 1.076 | 1.686 | 1.000 | 8.875 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5115.081 | 5210.766 | 0.99 | 0.045 | 0.036 |
# parameters of interest
params_lcs_consc_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_consc_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t3 ~ consc_t2", "d_consc_1 =~ consc_t3", "d_consc_1 ~ consc_t2", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t2 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t2 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t3 ~ consc_t2 | 1.000 | 1.000 | 1.000 | 1.039 | NA | NA |
| d_consc_1 =~ consc_t3 | 1.000 | 1.000 | 1.000 | 0.591 | NA | NA |
| d_consc_1 ~1 | 0.718 | 0.361 | 1.075 | 1.665 | 3.938 | 0.000 |
| consc_t2 ~1 | 3.507 | 3.413 | 3.602 | 4.624 | 72.538 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.163 | 0.081 | 0.245 | 0.877 | 3.887 | 0.000 |
| consc_t2 ~ frequ | 0.183 | 0.005 | 0.360 | 0.151 | 2.018 | 0.044 |
| d_consc_1 ~ consc_t2 | -0.201 | -0.297 | -0.105 | -0.354 | -4.111 | 0.000 |
| d_consc_1 ~ frequ | 0.020 | -0.089 | 0.130 | 0.030 | 0.366 | 0.714 |
| frequ ~~ frequ | 0.391 | 0.252 | 0.530 | 1.000 | 5.508 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5857.126 | 5952.81 | 0.99 | 0.045 | 0.028 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t3 ~ neuro_t2", "d_neuro_1 =~ neuro_t3", "d_neuro_1 ~ neuro_t2", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t2 ~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t2 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t3 ~ neuro_t2 | 1.000 | 1.000 | 1.000 | 1.024 | NA | NA |
| d_neuro_1 =~ neuro_t3 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_neuro_1 ~1 | 0.406 | 0.058 | 0.754 | 1.113 | 2.286 | 0.022 |
| neuro_t2 ~1 | 3.095 | 2.998 | 3.193 | 3.946 | 62.201 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.122 | 0.075 | 0.169 | 0.919 | 5.097 | 0.000 |
| neuro_t2 ~ goals | 0.465 | 0.365 | 0.565 | 0.651 | 9.124 | 0.000 |
| d_neuro_1 ~ neuro_t2 | -0.147 | -0.256 | -0.038 | -0.316 | -2.645 | 0.008 |
| d_neuro_1 ~ goals | 0.017 | -0.069 | 0.104 | 0.052 | 0.391 | 0.695 |
| goals ~~ goals | 1.204 | 0.869 | 1.539 | 1.000 | 7.045 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5257.822 | 5353.506 | 0.99 | 0.044 | 0.037 |
# parameters of interest
params_lcs_neuro_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_neuro_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t3 ~ neuro_t2", "d_neuro_1 =~ neuro_t3", "d_neuro_1 ~ neuro_t2", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t2 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t2 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t3 ~ neuro_t2 | 1.000 | 1.000 | 1.000 | 1.024 | NA | NA |
| d_neuro_1 =~ neuro_t3 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_neuro_1 ~1 | 0.362 | 0.148 | 0.575 | 0.991 | 3.323 | 0.001 |
| neuro_t2 ~1 | 3.095 | 2.998 | 3.193 | 3.947 | 62.215 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.122 | 0.075 | 0.169 | 0.919 | 5.099 | 0.000 |
| neuro_t2 ~ frequ | -0.078 | -0.256 | 0.099 | -0.063 | -0.866 | 0.386 |
| d_neuro_1 ~ neuro_t2 | -0.133 | -0.200 | -0.065 | -0.285 | -3.849 | 0.000 |
| d_neuro_1 ~ frequ | -0.023 | -0.128 | 0.082 | -0.040 | -0.431 | 0.667 |
| frequ ~~ frequ | 0.393 | 0.255 | 0.532 | 1.000 | 5.561 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 5307.825 | 5403.51 | 0.983 | 0.055 | 0.035 |
# parameters of interest
params_lcs_openn_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t3 ~ openn_t2", "d_openn_1 =~ openn_t3", "d_openn_1 ~ openn_t2", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t2 ~ goals", # change goals
"d_openn_1 ~1 ", "openn_t2 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t3 ~ openn_t2 | 1.000 | 1.000 | 1.000 | 1.007 | NA | NA |
| d_openn_1 =~ openn_t3 | 1.000 | 1.000 | 1.000 | 0.438 | NA | NA |
| d_openn_1 ~1 | 0.316 | -0.010 | 0.642 | 1.254 | 1.901 | 0.057 |
| openn_t2 ~1 | 3.586 | 3.505 | 3.667 | 6.190 | 87.225 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.059 | 0.033 | 0.085 | 0.929 | 4.393 | 0.000 |
| openn_t2 ~ goals | -0.225 | -0.331 | -0.119 | -0.307 | -4.172 | 0.000 |
| d_openn_1 ~ openn_t2 | -0.083 | -0.172 | 0.006 | -0.191 | -1.826 | 0.068 |
| d_openn_1 ~ goals | 0.043 | -0.020 | 0.107 | 0.136 | 1.336 | 0.182 |
| goals ~~ goals | 0.625 | 0.396 | 0.853 | 1.000 | 5.358 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 26 | 4883.383 | 4979.068 | 0.991 | 0.037 | 0.038 |
# parameters of interest
params_lcs_openn_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_openn_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t3 ~ openn_t2", "d_openn_1 =~ openn_t3", "d_openn_1 ~ openn_t2", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t2 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t2 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t3 ~ openn_t2 | 1.000 | 1.000 | 1.000 | 1.006 | NA | NA |
| d_openn_1 =~ openn_t3 | 1.000 | 1.000 | 1.000 | 0.440 | NA | NA |
| d_openn_1 ~1 | 0.370 | 0.039 | 0.701 | 1.480 | 2.193 | 0.028 |
| openn_t2 ~1 | 3.586 | 3.505 | 3.666 | 6.270 | 87.269 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.059 | 0.034 | 0.084 | 0.945 | 4.562 | 0.000 |
| openn_t2 ~ frequ | 0.263 | 0.112 | 0.413 | 0.287 | 3.422 | 0.001 |
| d_openn_1 ~ openn_t2 | -0.098 | -0.188 | -0.009 | -0.225 | -2.150 | 0.032 |
| d_openn_1 ~ frequ | -0.012 | -0.094 | 0.071 | -0.029 | -0.275 | 0.783 |
| frequ ~~ frequ | 0.389 | 0.250 | 0.528 | 1.000 | 5.487 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6473.45 | 6572.849 | 0.994 | 0.02 | 0.033 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # change goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.976 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_extra_1 ~1 | 0.668 | 0.153 | 1.182 | 2.786 | 2.544 | 0.011 |
| extra_t1 ~1 | 3.682 | 3.615 | 3.749 | 9.892 | 107.766 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.052 | 0.023 | 0.082 | 0.912 | 3.507 | 0.000 |
| extra_t1 ~ goals | 0.050 | -0.094 | 0.193 | 0.077 | 0.677 | 0.498 |
| d_extra_1 ~ extra_t1 | -0.187 | -0.324 | -0.049 | -0.290 | -2.652 | 0.008 |
| d_extra_1 ~ goals | 0.039 | -0.100 | 0.178 | 0.093 | 0.544 | 0.587 |
| goals ~~ goals | 0.332 | 0.012 | 0.652 | 1.000 | 2.037 | 0.042 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5410.908 | 5510.307 | 0.994 | 0.022 | 0.036 |
# parameters of interest
params_lcs_extra_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_extra_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.975 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.628 | NA | NA |
| d_extra_1 ~1 | 0.784 | 0.245 | 1.322 | 3.334 | 2.852 | 0.004 |
| extra_t1 ~1 | 3.682 | 3.615 | 3.749 | 10.096 | 107.889 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.049 | 0.021 | 0.077 | 0.884 | 3.414 | 0.001 |
| extra_t1 ~ frequ | 0.163 | 0.074 | 0.252 | 0.285 | 3.581 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.218 | -0.362 | -0.074 | -0.338 | -2.967 | 0.003 |
| d_extra_1 ~ frequ | 0.073 | -0.009 | 0.156 | 0.199 | 1.739 | 0.082 |
| frequ ~~ frequ | 0.408 | 0.272 | 0.545 | 1.000 | 5.857 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6269.053 | 6368.452 | 0.978 | 0.047 | 0.048 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # change goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.053 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.646 | NA | NA |
| d_agree_1 ~1 | 0.863 | 0.434 | 1.292 | 3.158 | 3.944 | 0.000 |
| agree_t1 ~1 | 3.770 | 3.700 | 3.839 | 8.453 | 106.066 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.063 | 0.036 | 0.091 | 0.850 | 4.513 | 0.000 |
| agree_t1 ~ goals | -0.054 | -0.126 | 0.017 | -0.114 | -1.487 | 0.137 |
| d_agree_1 ~ agree_t1 | -0.237 | -0.349 | -0.125 | -0.387 | -4.163 | 0.000 |
| d_agree_1 ~ goals | 0.001 | -0.055 | 0.057 | 0.004 | 0.038 | 0.969 |
| goals ~~ goals | 0.882 | 0.584 | 1.181 | 1.000 | 5.789 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5258.589 | 5357.988 | 0.97 | 0.056 | 0.052 |
# parameters of interest
params_lcs_agree_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_agree_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.055 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.647 | NA | NA |
| d_agree_1 ~1 | 0.875 | 0.401 | 1.349 | 3.262 | 3.618 | 0.000 |
| agree_t1 ~1 | 3.769 | 3.700 | 3.839 | 8.618 | 106.252 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.061 | 0.034 | 0.088 | 0.849 | 4.460 | 0.000 |
| agree_t1 ~ frequ | 0.166 | 0.069 | 0.263 | 0.242 | 3.363 | 0.001 |
| d_agree_1 ~ agree_t1 | -0.240 | -0.363 | -0.117 | -0.392 | -3.834 | 0.000 |
| d_agree_1 ~ frequ | 0.005 | -0.079 | 0.088 | 0.011 | 0.108 | 0.914 |
| frequ ~~ frequ | 0.406 | 0.266 | 0.546 | 1.000 | 5.693 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5871.279 | 5970.678 | 0.999 | 0.01 | 0.03 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # change goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.936 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_consc_1 ~1 | 0.642 | -0.058 | 1.342 | 2.418 | 1.797 | 0.072 |
| consc_t1 ~1 | 4.319 | 4.257 | 4.382 | 10.824 | 135.515 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.066 | 0.038 | 0.094 | 0.933 | 4.628 | 0.000 |
| consc_t1 ~ goals | 0.019 | -0.025 | 0.062 | 0.054 | 0.835 | 0.404 |
| d_consc_1 ~ consc_t1 | -0.146 | -0.304 | 0.011 | -0.220 | -1.820 | 0.069 |
| d_consc_1 ~ goals | -0.028 | -0.070 | 0.013 | -0.124 | -1.362 | 0.173 |
| goals ~~ goals | 1.336 | 1.025 | 1.646 | 1.000 | 8.433 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 4875.211 | 4974.61 | 0.982 | 0.044 | 0.053 |
# parameters of interest
params_lcs_consc_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_consc_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.937 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.619 | NA | NA |
| d_consc_1 ~1 | 0.746 | 0.001 | 1.491 | 2.805 | 1.964 | 0.050 |
| consc_t1 ~1 | 4.319 | 4.257 | 4.382 | 10.733 | 135.393 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.066 | 0.038 | 0.093 | 0.930 | 4.668 | 0.000 |
| consc_t1 ~ frequ | 0.144 | 0.057 | 0.230 | 0.229 | 3.246 | 0.001 |
| d_consc_1 ~ consc_t1 | -0.170 | -0.339 | -0.002 | -0.258 | -1.987 | 0.047 |
| d_consc_1 ~ frequ | 0.059 | -0.017 | 0.134 | 0.142 | 1.517 | 0.129 |
| frequ ~~ frequ | 0.412 | 0.270 | 0.555 | 1.000 | 5.676 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6034.885 | 6134.285 | 0.999 | 0.008 | 0.024 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # change goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.943 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.755 | NA | NA |
| d_neuro_1 ~1 | 0.463 | 0.168 | 0.759 | 1.270 | 3.072 | 0.002 |
| neuro_t1 ~1 | 1.707 | 1.648 | 1.766 | 3.748 | 56.783 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.118 | 0.077 | 0.159 | 0.887 | 5.641 | 0.000 |
| neuro_t1 ~ goals | -0.065 | -0.122 | -0.007 | -0.157 | -2.208 | 0.027 |
| d_neuro_1 ~ neuro_t1 | -0.247 | -0.429 | -0.064 | -0.308 | -2.653 | 0.008 |
| d_neuro_1 ~ goals | 0.031 | -0.021 | 0.083 | 0.094 | 1.159 | 0.246 |
| goals ~~ goals | 1.227 | 0.897 | 1.557 | 1.000 | 7.280 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 4896.303 | 4995.702 | 0.983 | 0.042 | 0.055 |
# parameters of interest
params_lcs_neuro_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_neuro_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.942 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.754 | NA | NA |
| d_neuro_1 ~1 | 0.498 | 0.177 | 0.820 | 1.367 | 3.037 | 0.002 |
| neuro_t1 ~1 | 1.707 | 1.648 | 1.766 | 3.746 | 56.783 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.119 | 0.077 | 0.160 | 0.892 | 5.637 | 0.000 |
| neuro_t1 ~ frequ | -0.136 | -0.248 | -0.024 | -0.192 | -2.376 | 0.017 |
| d_neuro_1 ~ neuro_t1 | -0.267 | -0.465 | -0.070 | -0.334 | -2.656 | 0.008 |
| d_neuro_1 ~ frequ | -0.041 | -0.143 | 0.061 | -0.072 | -0.785 | 0.432 |
| frequ ~~ frequ | 0.414 | 0.268 | 0.561 | 1.000 | 5.543 | 0.000 |
Results summary (goals = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 6172.404 | 6271.803 | 0.976 | 0.053 | 0.061 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # change goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.516 | NA | NA |
| d_openn_1 ~1 | 0.612 | 0.126 | 1.098 | 4.146 | 2.467 | 0.014 |
| openn_t1 ~1 | 3.917 | 3.856 | 3.979 | 13.356 | 125.080 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.020 | 0.007 | 0.032 | 0.909 | 3.080 | 0.002 |
| openn_t1 ~ goals | -0.011 | -0.066 | 0.043 | -0.031 | -0.415 | 0.678 |
| d_openn_1 ~ openn_t1 | -0.152 | -0.273 | -0.031 | -0.302 | -2.461 | 0.014 |
| d_openn_1 ~ goals | -0.002 | -0.038 | 0.035 | -0.009 | -0.091 | 0.927 |
| goals ~~ goals | 0.629 | 0.417 | 0.842 | 1.000 | 5.801 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 26 | 5335.677 | 5435.077 | 0.949 | 0.071 | 0.072 |
# parameters of interest
params_lcs_openn_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_openn_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.033 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.516 | NA | NA |
| d_openn_1 ~1 | 0.647 | 0.151 | 1.142 | 4.323 | 2.558 | 0.011 |
| openn_t1 ~1 | 3.917 | 3.856 | 3.979 | 13.070 | 124.999 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.020 | 0.007 | 0.033 | 0.895 | 3.037 | 0.002 |
| openn_t1 ~ frequ | 0.056 | -0.025 | 0.136 | 0.118 | 1.363 | 0.173 |
| d_openn_1 ~ openn_t1 | -0.161 | -0.284 | -0.038 | -0.322 | -2.557 | 0.011 |
| d_openn_1 ~ frequ | 0.021 | -0.032 | 0.073 | 0.087 | 0.775 | 0.438 |
| frequ ~~ frequ | 0.400 | 0.260 | 0.539 | 1.000 | 5.613 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific change goal
facet_template_mod_goal <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets -> change in current-self ratings
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
mod_name = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), facet_model_fit))
}
# loop across 15 facets -> change in ideal-self ratings
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[6]], item_nrs) # ideal
mod_name = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp6")), template_filled)) # ideal
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp6")), facet_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for current self ratings)
facet_template_mod_goal_fu <- '
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2
facet_t3 =~ 1*ind1_t3 + lamb2*ind2_t3 + lamb3*ind3_t3 + lamb4*ind4_t3 # This specifies the measurement model for facet at T3 (with equality constraints)
facet_t3 ~ 1*facet_t2 # This parameter regresses facet_t3 perfectly on facet_t2
d_facet_1 =~ 1*facet_t3 # This defines the latent change score factor as measured perfectly by scores on facet_t3
facet_t3 ~ 0*1 # This line constrains the intercept of facet_t3 to 0
facet_t3 ~~ 0*facet_t3 # This fixes the variance of facet_t3 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t2 ~ 1 # This estimates the intercept of facet_t2
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t2 ~~ facet_t2 # This estimates the variance of facet_t2
facet_t2 ~ ind_goal # This estimates the moderation effect on personality at T2
d_facet_1 ~ facet_t2 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t2 ~~ ind1_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind2_t2 ~~ ind2_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind3_t2 ~~ ind3_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind4_t2 ~~ ind4_t3 # This allows residual covariance on indicator X4 across T2 and T3
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t3 ~~ res1*ind1_t3 # This allows residual variance on indicator X1 at T3
ind2_t3 ~~ res2*ind2_t3 # This allows residual variance on indicator X2 at T3
ind3_t3 ~~ res3*ind3_t3 # This allows residual variance on indicator X3 at T3
ind4_t3 ~~ res4*ind4_t3 # This allows residual variance on indicator X4 at T3
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind2_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind3_t3 ~ m3*1 # This estimates the intercept of X3 at T3
ind4_t3 ~ m4*1 # This estimates the intercept of X4 at T3
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets -> change in current-self ratings
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
mod_name = paste0("sb07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal_fu,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp6_fu")), facet_model_fit))
}
# 2nd, for frequency of self-improvement behavior
facet_template_mod_frequ <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 15 facets -> change in current-self ratings
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), facet_model_fit))
}
# loop across 15 facets -> change in ideal-self ratings
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[6]], item_nrs) # ideal
template_filled <- str_replace_all(facet_template_mod_frequ,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp6")), template_filled)) # ideal
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp6")), facet_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for current self ratings)
facet_template_mod_frequ_fu <- '
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2
facet_t3 =~ 1*ind1_t3 + lamb2*ind2_t3 + lamb3*ind3_t3 + lamb4*ind4_t3 # This specifies the measurement model for facet at T3 (with equality constraints)
frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderator
facet_t3 ~ 1*facet_t2 # This parameter regresses facet_t3 perfectly on facet_t2
d_facet_1 =~ 1*facet_t3 # This defines the latent change score factor as measured perfectly by scores on facet_t3
facet_t3 ~ 0*1 # This line constrains the intercept of facet_t3 to 0
facet_t3 ~~ 0*facet_t3 # This fixes the variance of facet_t3 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t2 ~ 1 # This estimates the intercept of facet_t2
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t2 ~~ facet_t2 # This estimates the variance of facet_t2
facet_t2 ~ frequ # This estimates the moderation effect on personality at T2
d_facet_1 ~ facet_t2 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t2 ~~ ind1_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind2_t2 ~~ ind2_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind3_t2 ~~ ind3_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind4_t2 ~~ ind4_t3 # This allows residual covariance on indicator X4 across T2 and T3
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t3 ~~ res1*ind1_t3 # This allows residual variance on indicator X1 at T3
ind2_t3 ~~ res2*ind2_t3 # This allows residual variance on indicator X2 at T3
ind3_t3 ~~ res3*ind3_t3 # This allows residual variance on indicator X3 at T3
ind4_t3 ~~ res4*ind4_t3 # This allows residual variance on indicator X4 at T3
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind2_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind3_t3 ~ m3*1 # This estimates the intercept of X3 at T3
ind4_t3 ~ m4*1 # This estimates the intercept of X4 at T3
sb04_01_t2 ~~ sb04_01_t2
sb04_02_t2 ~~ sb04_02_t2
sb04_03_t2 ~~ sb04_03_t2
sb04_01_t2 ~ 1
sb04_02_t2 ~ 1
sb04_03_t2 ~ 1
'
# loop across 15 facets -> change in current-self ratings from T2 to T3
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post current
items = paste0(bfi_versions[[5]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ_fu,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sb_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6_fu")), facet_model_fit))
} Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8035.567 | 8123.497 | 0.937 | 0.088 | 0.052 |
# parameters of interest
params_lcs_socia_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", "d_socia_1 ~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.078 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.504 | NA | NA |
| d_socia_1 ~1 | 0.795 | 0.325 | 1.266 | 2.026 | 3.315 | 0.001 |
| socia_t1 ~1 | 3.987 | 3.643 | 4.331 | 4.748 | 22.721 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.130 | 0.067 | 0.193 | 0.843 | 4.045 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.205 | -0.309 | -0.101 | -0.438 | -3.851 | 0.000 |
| d_socia_1 ~ sb07_01_t1 | -0.036 | -0.101 | 0.029 | -0.116 | -1.080 | 0.280 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.596 | 1.400 | 1.793 | 1.000 | 15.904 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9260.587 | 9375.278 | 0.938 | 0.075 | 0.066 |
# parameters of interest
params_lcs_socia_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_socia_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.082 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.505 | NA | NA |
| d_socia_1 ~1 | 0.547 | 0.305 | 0.789 | 1.439 | 4.426 | 0.000 |
| socia_t1 ~1 | 2.875 | 2.747 | 3.003 | 3.530 | 44.063 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.108 | 0.051 | 0.165 | 0.748 | 3.700 | 0.000 |
| socia_t1 ~ frequ | -0.147 | -0.336 | 0.042 | -0.114 | -1.520 | 0.129 |
| d_socia_1 ~ socia_t1 | -0.165 | -0.252 | -0.078 | -0.353 | -3.717 | 0.000 |
| d_socia_1 ~ frequ | 0.191 | 0.077 | 0.304 | 0.319 | 3.299 | 0.001 |
| frequ ~~ frequ | 0.403 | 0.264 | 0.542 | 1.000 | 5.689 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8326.72 | 8414.65 | 0.94 | 0.076 | 0.056 |
# parameters of interest
params_lcs_asser_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", "d_asser_1 ~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.482 | NA | NA |
| d_asser_1 ~1 | 0.669 | 0.314 | 1.024 | 2.406 | 3.692 | 0.000 |
| asser_t1 ~1 | 3.480 | 3.226 | 3.735 | 5.824 | 26.773 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.068 | 0.014 | 0.123 | 0.885 | 2.446 | 0.014 |
| d_asser_1 ~ asser_t1 | -0.160 | -0.256 | -0.063 | -0.344 | -3.250 | 0.001 |
| d_asser_1 ~ sb07_02_t1 | -0.033 | -0.070 | 0.005 | -0.153 | -1.696 | 0.090 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.688 | 1.495 | 1.880 | 1.000 | 17.186 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9480.491 | 9595.182 | 0.981 | 0.038 | 0.041 |
# parameters of interest
params_lcs_asser_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_asser_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.486 | NA | NA |
| d_asser_1 ~1 | 0.528 | 0.229 | 0.826 | 1.960 | 3.462 | 0.001 |
| asser_t1 ~1 | 3.094 | 2.974 | 3.215 | 5.455 | 50.439 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.065 | 0.015 | 0.115 | 0.896 | 2.558 | 0.011 |
| asser_t1 ~ frequ | 0.195 | 0.048 | 0.342 | 0.215 | 2.597 | 0.009 |
| d_asser_1 ~ asser_t1 | -0.152 | -0.247 | -0.056 | -0.320 | -3.119 | 0.002 |
| d_asser_1 ~ frequ | 0.063 | -0.024 | 0.149 | 0.146 | 1.422 | 0.155 |
| frequ ~~ frequ | 0.391 | 0.255 | 0.528 | 1.000 | 5.624 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8428.622 | 8516.552 | 0.978 | 0.042 | 0.039 |
# parameters of interest
params_lcs_energ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", "d_energ_1 ~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.075 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.481 | NA | NA |
| d_energ_1 ~1 | 0.374 | 0.082 | 0.666 | 2.473 | 2.510 | 0.012 |
| energ_t1 ~1 | 2.309 | 2.079 | 2.539 | 6.830 | 19.653 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.019 | -0.001 | 0.039 | 0.822 | 1.852 | 0.064 |
| d_energ_1 ~ energ_t1 | -0.121 | -0.244 | 0.003 | -0.270 | -1.920 | 0.055 |
| d_energ_1 ~ sb07_03_t1 | -0.027 | -0.055 | 0.002 | -0.220 | -1.841 | 0.066 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.571 | 1.373 | 1.769 | 1.000 | 15.560 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9657.041 | 9771.733 | 0.968 | 0.045 | 0.051 |
# parameters of interest
params_lcs_energ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_energ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.077 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.487 | NA | NA |
| d_energ_1 ~1 | 0.464 | 0.179 | 0.749 | 2.948 | 3.195 | 0.001 |
| energ_t1 ~1 | 2.771 | 2.663 | 2.880 | 7.954 | 50.194 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.019 | -0.001 | 0.039 | 0.757 | 1.827 | 0.068 |
| energ_t1 ~ frequ | -0.068 | -0.168 | 0.032 | -0.121 | -1.332 | 0.183 |
| d_energ_1 ~ energ_t1 | -0.189 | -0.292 | -0.085 | -0.417 | -3.586 | 0.000 |
| d_energ_1 ~ frequ | -0.081 | -0.146 | -0.015 | -0.317 | -2.410 | 0.016 |
| frequ ~~ frequ | 0.383 | 0.244 | 0.522 | 1.000 | 5.407 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8251.835 | 8339.765 | 0.969 | 0.043 | 0.044 |
# parameters of interest
params_lcs_compa_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", "d_compa_1 ~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.132 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.462 | NA | NA |
| d_compa_1 ~1 | 0.795 | 0.237 | 1.353 | 3.204 | 2.793 | 0.005 |
| compa_t1 ~1 | 4.594 | 4.429 | 4.760 | 7.562 | 54.393 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.047 | 0.001 | 0.093 | 0.767 | 2.001 | 0.045 |
| d_compa_1 ~ compa_t1 | -0.178 | -0.295 | -0.061 | -0.435 | -2.972 | 0.003 |
| d_compa_1 ~ sb07_04_t1 | 0.019 | -0.026 | 0.065 | 0.106 | 0.833 | 0.405 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.843 | 1.662 | 2.024 | 1.000 | 19.989 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9379.957 | 9494.648 | 0.932 | 0.062 | 0.07 |
# parameters of interest
params_lcs_compa_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_compa_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.133 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.464 | NA | NA |
| d_compa_1 ~1 | 1.006 | 0.486 | 1.526 | 4.012 | 3.793 | 0.000 |
| compa_t1 ~1 | 4.183 | 4.090 | 4.276 | 6.834 | 88.071 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.047 | 0.000 | 0.093 | 0.742 | 1.981 | 0.048 |
| compa_t1 ~ frequ | 0.269 | 0.084 | 0.453 | 0.278 | 2.855 | 0.004 |
| d_compa_1 ~ compa_t1 | -0.216 | -0.336 | -0.096 | -0.527 | -3.534 | 0.000 |
| d_compa_1 ~ frequ | 0.075 | -0.043 | 0.193 | 0.189 | 1.244 | 0.214 |
| frequ ~~ frequ | 0.400 | 0.259 | 0.542 | 1.000 | 5.544 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7156.733 | 7244.663 | 0.951 | 0.064 | 0.062 |
# parameters of interest
params_lcs_respe_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", "d_respe_1 ~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.166 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.637 | NA | NA |
| d_respe_1 ~1 | 1.399 | 0.662 | 2.136 | 4.481 | 3.719 | 0.000 |
| respe_t1 ~1 | 4.849 | 4.719 | 4.979 | 8.483 | 73.056 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.071 | 0.030 | 0.112 | 0.729 | 3.376 | 0.001 |
| d_respe_1 ~ respe_t1 | -0.299 | -0.450 | -0.147 | -0.547 | -3.859 | 0.000 |
| d_respe_1 ~ sb07_05_t1 | -0.019 | -0.059 | 0.022 | -0.081 | -0.911 | 0.362 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.826 | 1.606 | 2.045 | 1.000 | 16.293 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 8302.649 | 8417.341 | 0.954 | 0.056 | 0.06 |
# parameters of interest
params_lcs_respe_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respe_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.167 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.639 | NA | NA |
| d_respe_1 ~1 | 1.243 | 0.540 | 1.946 | 3.977 | 3.464 | 0.001 |
| respe_t1 ~1 | 4.474 | 4.395 | 4.552 | 7.847 | 111.809 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.071 | 0.030 | 0.112 | 0.731 | 3.414 | 0.001 |
| respe_t1 ~ frequ | 0.286 | 0.142 | 0.431 | 0.316 | 3.880 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.274 | -0.427 | -0.120 | -0.500 | -3.495 | 0.000 |
| d_respe_1 ~ frequ | -0.026 | -0.122 | 0.070 | -0.053 | -0.535 | 0.593 |
| frequ ~~ frequ | 0.396 | 0.261 | 0.531 | 1.000 | 5.753 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8531.227 | 8619.157 | 0.95 | 0.059 | 0.056 |
# parameters of interest
params_lcs_trust_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", "d_trust_1 ~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.514 | NA | NA |
| d_trust_1 ~1 | 0.463 | 0.127 | 0.799 | 1.476 | 2.702 | 0.007 |
| trust_t1 ~1 | 2.586 | 2.344 | 2.828 | 3.946 | 20.933 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.083 | 0.016 | 0.150 | 0.839 | 2.418 | 0.016 |
| d_trust_1 ~ trust_t1 | -0.168 | -0.284 | -0.052 | -0.351 | -2.837 | 0.005 |
| d_trust_1 ~ sb07_06_t1 | -0.032 | -0.084 | 0.020 | -0.138 | -1.211 | 0.226 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.828 | 1.657 | 2.000 | 1.000 | 20.862 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9655.017 | 9769.708 | 0.966 | 0.045 | 0.058 |
# parameters of interest
params_lcs_trust_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_trust_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.077 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.503 | NA | NA |
| d_trust_1 ~1 | 0.421 | 0.099 | 0.743 | 1.443 | 2.560 | 0.010 |
| trust_t1 ~1 | 2.834 | 2.710 | 2.958 | 4.540 | 44.770 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.073 | 0.005 | 0.140 | 0.853 | 2.120 | 0.034 |
| trust_t1 ~ frequ | -0.126 | -0.303 | 0.052 | -0.127 | -1.387 | 0.165 |
| d_trust_1 ~ trust_t1 | -0.181 | -0.296 | -0.066 | -0.387 | -3.088 | 0.002 |
| d_trust_1 ~ frequ | -0.022 | -0.131 | 0.087 | -0.047 | -0.395 | 0.693 |
| frequ ~~ frequ | 0.395 | 0.258 | 0.531 | 1.000 | 5.656 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7983.001 | 8070.931 | 0.957 | 0.076 | 0.043 |
# parameters of interest
params_lcs_organ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", "d_organ_1 ~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.446 | NA | NA |
| d_organ_1 ~1 | 0.224 | -0.013 | 0.461 | 0.457 | 1.853 | 0.064 |
| organ_t1 ~1 | 1.266 | 0.956 | 1.577 | 1.133 | 7.992 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.225 | 0.092 | 0.359 | 0.935 | 3.302 | 0.001 |
| d_organ_1 ~ organ_t1 | -0.111 | -0.239 | 0.017 | -0.252 | -1.694 | 0.090 |
| d_organ_1 ~ sb07_07_t1 | -0.001 | -0.085 | 0.083 | -0.003 | -0.027 | 0.979 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.197 | 2.005 | 2.390 | 1.000 | 22.378 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9180.5 | 9295.191 | 0.977 | 0.047 | 0.04 |
# parameters of interest
params_lcs_organ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_organ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.454 | NA | NA |
| d_organ_1 ~1 | 0.250 | -0.006 | 0.505 | 0.544 | 1.915 | 0.056 |
| organ_t1 ~1 | 2.856 | 2.708 | 3.003 | 2.786 | 37.969 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.196 | 0.082 | 0.309 | 0.930 | 3.369 | 0.001 |
| organ_t1 ~ frequ | -0.214 | -0.455 | 0.027 | -0.131 | -1.742 | 0.082 |
| d_organ_1 ~ organ_t1 | -0.118 | -0.208 | -0.028 | -0.264 | -2.571 | 0.010 |
| d_organ_1 ~ frequ | -0.054 | -0.193 | 0.086 | -0.074 | -0.756 | 0.450 |
| frequ ~~ frequ | 0.395 | 0.257 | 0.534 | 1.000 | 5.590 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7978.471 | 8066.401 | 0.953 | 0.075 | 0.055 |
# parameters of interest
params_lcs_produ_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", "d_produ_1 ~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.019 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.651 | NA | NA |
| d_produ_1 ~1 | 0.551 | 0.169 | 0.933 | 0.908 | 2.824 | 0.005 |
| produ_t1 ~1 | 1.763 | 1.422 | 2.105 | 1.857 | 10.120 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.318 | 0.167 | 0.469 | 0.863 | 4.119 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.289 | -0.442 | -0.135 | -0.451 | -3.693 | 0.000 |
| d_produ_1 ~ sb07_08_t1 | 0.071 | -0.017 | 0.160 | 0.162 | 1.585 | 0.113 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.898 | 1.650 | 2.147 | 1.000 | 14.989 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9215.556 | 9330.247 | 0.956 | 0.062 | 0.053 |
# parameters of interest
params_lcs_produ_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_produ_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.022 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.657 | NA | NA |
| d_produ_1 ~1 | 0.656 | 0.249 | 1.064 | 1.095 | 3.157 | 0.002 |
| produ_t1 ~1 | 3.417 | 3.283 | 3.550 | 3.664 | 50.196 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.305 | 0.150 | 0.460 | 0.847 | 3.850 | 0.000 |
| produ_t1 ~ frequ | -0.166 | -0.396 | 0.065 | -0.111 | -1.406 | 0.160 |
| d_produ_1 ~ produ_t1 | -0.240 | -0.356 | -0.123 | -0.373 | -4.032 | 0.000 |
| d_produ_1 ~ frequ | -0.159 | -0.320 | 0.003 | -0.166 | -1.925 | 0.054 |
| frequ ~~ frequ | 0.391 | 0.252 | 0.531 | 1.000 | 5.492 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8278.143 | 8366.073 | 0.922 | 0.082 | 0.077 |
# parameters of interest
params_lcs_respo_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", "d_respo_1 ~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.043 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.550 | NA | NA |
| d_respo_1 ~1 | 0.764 | 0.220 | 1.307 | 3.007 | 2.755 | 0.006 |
| respo_t1 ~1 | 4.055 | 3.858 | 4.253 | 8.412 | 40.172 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.057 | 0.013 | 0.101 | 0.883 | 2.560 | 0.010 |
| d_respo_1 ~ respo_t1 | -0.190 | -0.317 | -0.063 | -0.360 | -2.922 | 0.003 |
| d_respo_1 ~ sb07_09_t1 | -0.008 | -0.046 | 0.030 | -0.044 | -0.393 | 0.694 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.197 | 2.012 | 2.381 | 1.000 | 23.307 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9394.541 | 9509.232 | 0.921 | 0.074 | 0.08 |
# parameters of interest
params_lcs_respo_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respo_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.043 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.551 | NA | NA |
| d_respo_1 ~1 | 0.708 | 0.330 | 1.085 | 2.722 | 3.677 | 0.000 |
| respo_t1 ~1 | 3.598 | 3.493 | 3.703 | 7.308 | 67.244 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.060 | 0.013 | 0.107 | 0.883 | 2.489 | 0.013 |
| respo_t1 ~ frequ | 0.052 | -0.080 | 0.184 | 0.066 | 0.771 | 0.441 |
| d_respo_1 ~ respo_t1 | -0.181 | -0.283 | -0.078 | -0.342 | -3.456 | 0.001 |
| d_respo_1 ~ frequ | 0.010 | -0.081 | 0.101 | 0.024 | 0.217 | 0.829 |
| frequ ~~ frequ | 0.394 | 0.255 | 0.532 | 1.000 | 5.570 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8282.415 | 8370.345 | 0.993 | 0.026 | 0.037 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", "d_anxie_1 ~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.473 | NA | NA |
| d_anxie_1 ~1 | 0.009 | -0.671 | 0.690 | 0.022 | 0.027 | 0.979 |
| anxie_t1 ~1 | 4.233 | 3.930 | 4.535 | 4.572 | 27.434 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.158 | 0.062 | 0.254 | 0.868 | 3.238 | 0.001 |
| d_anxie_1 ~ anxie_t1 | -0.039 | -0.189 | 0.110 | -0.085 | -0.517 | 0.605 |
| d_anxie_1 ~ sb07_10_t1 | 0.085 | 0.000 | 0.171 | 0.301 | 1.957 | 0.050 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.277 | 2.041 | 2.512 | 1.000 | 18.979 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9463.499 | 9578.19 | 0.977 | 0.04 | 0.046 |
# parameters of interest
params_lcs_anxie_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_anxie_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.029 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.472 | NA | NA |
| d_anxie_1 ~1 | 0.581 | 0.291 | 0.870 | 1.344 | 3.931 | 0.000 |
| anxie_t1 ~1 | 2.748 | 2.613 | 2.883 | 2.913 | 39.911 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.170 | 0.073 | 0.266 | 0.908 | 3.438 | 0.001 |
| anxie_t1 ~ frequ | -0.028 | -0.266 | 0.209 | -0.019 | -0.234 | 0.815 |
| d_anxie_1 ~ anxie_t1 | -0.132 | -0.231 | -0.034 | -0.289 | -2.637 | 0.008 |
| d_anxie_1 ~ frequ | 0.059 | -0.100 | 0.218 | 0.086 | 0.727 | 0.467 |
| frequ ~~ frequ | 0.398 | 0.258 | 0.539 | 1.000 | 5.553 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8187.961 | 8275.891 | 0.956 | 0.079 | 0.055 |
# parameters of interest
params_lcs_depre_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", "d_depre_1 ~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.055 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.583 | NA | NA |
| d_depre_1 ~1 | 1.192 | 0.525 | 1.859 | 3.303 | 3.503 | 0.000 |
| depre_t1 ~1 | 4.028 | 3.839 | 4.217 | 6.168 | 41.841 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.109 | 0.060 | 0.159 | 0.840 | 4.354 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.299 | -0.460 | -0.137 | -0.540 | -3.620 | 0.000 |
| d_depre_1 ~ sb07_11_t1 | -0.052 | -0.113 | 0.008 | -0.234 | -1.691 | 0.091 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.607 | 2.417 | 2.797 | 1.000 | 26.876 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9417.201 | 9531.892 | 0.928 | 0.084 | 0.079 |
# parameters of interest
params_lcs_depre_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_depre_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.057 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.588 | NA | NA |
| d_depre_1 ~1 | 0.744 | 0.471 | 1.017 | 2.000 | 5.347 | 0.000 |
| depre_t1 ~1 | 3.037 | 2.916 | 3.158 | 4.539 | 49.287 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.119 | 0.064 | 0.174 | 0.858 | 4.225 | 0.000 |
| depre_t1 ~ frequ | -0.015 | -0.177 | 0.147 | -0.014 | -0.181 | 0.857 |
| d_depre_1 ~ depre_t1 | -0.206 | -0.295 | -0.118 | -0.371 | -4.578 | 0.000 |
| d_depre_1 ~ frequ | 0.034 | -0.063 | 0.130 | 0.057 | 0.680 | 0.496 |
| frequ ~~ frequ | 0.395 | 0.254 | 0.536 | 1.000 | 5.497 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8350.284 | 8438.215 | 0.989 | 0.036 | 0.032 |
# parameters of interest
params_lcs_volat_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", "d_volat_1 ~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.159 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.666 | NA | NA |
| d_volat_1 ~1 | 0.679 | 0.407 | 0.951 | 1.187 | 4.888 | 0.000 |
| volat_t1 ~1 | 2.471 | 2.224 | 2.718 | 2.485 | 19.599 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.238 | 0.148 | 0.328 | 0.728 | 5.172 | 0.000 |
| d_volat_1 ~ volat_t1 | -0.317 | -0.411 | -0.223 | -0.551 | -6.593 | 0.000 |
| d_volat_1 ~ sb07_12_t1 | 0.048 | -0.016 | 0.111 | 0.121 | 1.460 | 0.144 |
| sb07_12_t1 ~~ sb07_12_t1 | 2.103 | 1.911 | 2.296 | 1.000 | 21.428 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9461.255 | 9575.946 | 1 | 0 | 0.028 |
# parameters of interest
params_lcs_volat_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_volat_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.158 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.664 | NA | NA |
| d_volat_1 ~1 | 0.730 | 0.459 | 1.001 | 1.289 | 5.271 | 0.000 |
| volat_t1 ~1 | 3.117 | 2.978 | 3.256 | 3.161 | 43.892 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.237 | 0.147 | 0.328 | 0.741 | 5.129 | 0.000 |
| volat_t1 ~ frequ | -0.055 | -0.285 | 0.175 | -0.035 | -0.466 | 0.641 |
| d_volat_1 ~ volat_t1 | -0.291 | -0.378 | -0.204 | -0.507 | -6.522 | 0.000 |
| d_volat_1 ~ frequ | 0.028 | -0.124 | 0.179 | 0.031 | 0.358 | 0.720 |
| frequ ~~ frequ | 0.396 | 0.259 | 0.533 | 1.000 | 5.651 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8110.242 | 8198.172 | 0.994 | 0.02 | 0.037 |
# parameters of interest
params_lcs_curio_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", "d_curio_1 ~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.992 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.434 | NA | NA |
| d_curio_1 ~1 | 0.148 | -0.590 | 0.886 | 0.768 | 0.394 | 0.694 |
| curio_t1 ~1 | 4.460 | 4.315 | 4.605 | 10.107 | 60.268 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.034 | -0.002 | 0.069 | 0.902 | 1.851 | 0.064 |
| d_curio_1 ~ curio_t1 | -0.048 | -0.209 | 0.113 | -0.109 | -0.581 | 0.561 |
| d_curio_1 ~ sb07_13_t1 | 0.038 | -0.004 | 0.080 | 0.257 | 1.783 | 0.075 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.720 | 1.528 | 1.912 | 1.000 | 17.525 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9264.735 | 9379.427 | 0.981 | 0.033 | 0.051 |
# parameters of interest
params_lcs_curio_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_curio_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.429 | NA | NA |
| d_curio_1 ~1 | 0.472 | -0.129 | 1.072 | 2.467 | 1.540 | 0.124 |
| curio_t1 ~1 | 4.182 | 4.095 | 4.269 | 9.450 | 93.719 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.029 | -0.003 | 0.062 | 0.804 | 1.775 | 0.076 |
| curio_t1 ~ frequ | 0.071 | -0.057 | 0.199 | 0.101 | 1.091 | 0.275 |
| d_curio_1 ~ curio_t1 | -0.104 | -0.243 | 0.036 | -0.240 | -1.459 | 0.145 |
| d_curio_1 ~ frequ | 0.121 | 0.034 | 0.208 | 0.397 | 2.718 | 0.007 |
| frequ ~~ frequ | 0.394 | 0.255 | 0.533 | 1.000 | 5.559 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8395.355 | 8483.285 | 0.999 | 0.01 | 0.035 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", "d_aesth_1 ~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_aesth_1 ~1 | 0.340 | 0.059 | 0.620 | 3.438 | 2.370 | 0.018 |
| aesth_t1 ~1 | 2.929 | 2.795 | 3.062 | 14.460 | 42.966 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.009 | -0.005 | 0.024 | 0.939 | 1.245 | 0.213 |
| d_aesth_1 ~ aesth_t1 | -0.120 | -0.216 | -0.023 | -0.245 | -2.427 | 0.015 |
| d_aesth_1 ~ sb07_14_t1 | 0.002 | -0.011 | 0.014 | 0.023 | 0.254 | 0.800 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.960 | 1.782 | 2.138 | 1.000 | 21.634 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9481.735 | 9596.426 | 0.99 | 0.028 | 0.047 |
# parameters of interest
params_lcs_aesth_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_aesth_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 1.000 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_aesth_1 ~1 | 0.341 | 0.042 | 0.640 | 3.407 | 2.233 | 0.026 |
| aesth_t1 ~1 | 2.934 | 2.810 | 3.058 | 14.304 | 46.393 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.009 | -0.005 | 0.024 | 0.941 | 1.258 | 0.209 |
| aesth_t1 ~ frequ | -0.071 | -0.147 | 0.006 | -0.216 | -1.806 | 0.071 |
| d_aesth_1 ~ aesth_t1 | -0.119 | -0.222 | -0.015 | -0.243 | -2.247 | 0.025 |
| d_aesth_1 ~ frequ | 0.000 | -0.034 | 0.034 | 0.001 | 0.005 | 0.996 |
| frequ ~~ frequ | 0.394 | 0.253 | 0.535 | 1.000 | 5.463 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7978.148 | 8066.078 | 0.951 | 0.065 | 0.053 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp6 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", "d_imagi_1 ~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.481 | NA | NA |
| d_imagi_1 ~1 | 0.684 | 0.134 | 1.234 | 2.021 | 2.439 | 0.015 |
| imagi_t1 ~1 | 4.176 | 3.985 | 4.367 | 5.815 | 42.897 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.105 | 0.042 | 0.169 | 0.919 | 3.250 | 0.001 |
| d_imagi_1 ~ imagi_t1 | -0.143 | -0.264 | -0.021 | -0.303 | -2.304 | 0.021 |
| d_imagi_1 ~ sb07_15_t1 | -0.018 | -0.068 | 0.032 | -0.075 | -0.695 | 0.487 |
| sb07_15_t1 ~~ sb07_15_t1 | 2.017 | 1.837 | 2.198 | 1.000 | 21.908 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9083.231 | 9197.922 | 0.962 | 0.052 | 0.052 |
# parameters of interest
params_lcs_imagi_curr_frequ_hyp6 <- broom::tidy(fit_mi_lcs_imagi_curr_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.023 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_imagi_1 ~1 | 0.691 | 0.254 | 1.127 | 1.982 | 3.102 | 0.002 |
| imagi_t1 ~1 | 3.644 | 3.544 | 3.744 | 4.989 | 71.301 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.107 | 0.042 | 0.171 | 0.879 | 3.249 | 0.001 |
| imagi_t1 ~ frequ | 0.277 | 0.085 | 0.469 | 0.237 | 2.821 | 0.005 |
| d_imagi_1 ~ imagi_t1 | -0.159 | -0.274 | -0.045 | -0.334 | -2.726 | 0.006 |
| d_imagi_1 ~ frequ | 0.114 | -0.012 | 0.241 | 0.205 | 1.777 | 0.076 |
| frequ ~~ frequ | 0.389 | 0.256 | 0.523 | 1.000 | 5.707 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 6930.966 | 7015.61 | 0.965 | 0.066 | 0.049 |
# parameters of interest
params_lcs_socia_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t3 ~ socia_t2", "d_socia_1 =~ socia_t3", "d_socia_1 ~ socia_t2", # change parameters
"d_socia_1 ~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", "d_socia_1 ~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t2 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t3 ~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.974 | NA | NA |
| d_socia_1 =~ socia_t3 | 1.000 | 1.000 | 1.000 | 0.475 | NA | NA |
| d_socia_1 ~1 | 0.363 | -0.135 | 0.862 | 1.008 | 1.429 | 0.153 |
| socia_t2 ~1 | 4.005 | 3.678 | 4.332 | 5.415 | 24.014 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.125 | 0.060 | 0.190 | 0.964 | 3.772 | 0.000 |
| d_socia_1 ~ socia_t2 | -0.096 | -0.207 | 0.015 | -0.197 | -1.698 | 0.090 |
| d_socia_1 ~ sb07_01_t1 | -0.006 | -0.073 | 0.061 | -0.021 | -0.175 | 0.861 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.587 | 1.374 | 1.800 | 1.000 | 14.600 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8296.975 | 8407.38 | 0.954 | 0.067 | 0.069 |
# parameters of interest
params_lcs_socia_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_socia_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t3 ~ socia_t2", "d_socia_1 =~ socia_t3", "d_socia_1 ~ socia_t2", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t2 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t2 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t3 ~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.973 | NA | NA |
| d_socia_1 =~ socia_t3 | 1.000 | 1.000 | 1.000 | 0.475 | NA | NA |
| d_socia_1 ~1 | 0.324 | 0.033 | 0.615 | 0.920 | 2.180 | 0.029 |
| socia_t2 ~1 | 3.059 | 2.925 | 3.193 | 4.242 | 44.839 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.119 | 0.055 | 0.184 | 0.964 | 3.637 | 0.000 |
| socia_t2 ~ frequ | 0.028 | -0.154 | 0.211 | 0.025 | 0.303 | 0.762 |
| d_socia_1 ~ socia_t2 | -0.091 | -0.187 | 0.005 | -0.186 | -1.848 | 0.065 |
| d_socia_1 ~ frequ | -0.021 | -0.134 | 0.093 | -0.037 | -0.357 | 0.721 |
| frequ ~~ frequ | 0.397 | 0.256 | 0.538 | 1.000 | 5.524 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7262.607 | 7347.251 | 0.962 | 0.059 | 0.05 |
# parameters of interest
params_lcs_asser_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t3 ~ asser_t2", "d_asser_1 =~ asser_t3", "d_asser_1 ~ asser_t2", # change parameters
"d_asser_1 ~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", "d_asser_1 ~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t2 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t3 ~ asser_t2 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_asser_1 =~ asser_t3 | 1.000 | 1.000 | 1.000 | 0.494 | NA | NA |
| d_asser_1 ~1 | 0.329 | -0.099 | 0.757 | 1.211 | 1.506 | 0.132 |
| asser_t2 ~1 | 3.505 | 3.225 | 3.784 | 6.371 | 24.599 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.069 | 0.008 | 0.130 | 0.933 | 2.224 | 0.026 |
| d_asser_1 ~ asser_t2 | -0.114 | -0.229 | 0.001 | -0.231 | -1.938 | 0.053 |
| d_asser_1 ~ sb07_02_t1 | 0.016 | -0.025 | 0.056 | 0.075 | 0.765 | 0.444 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.689 | 1.486 | 1.891 | 1.000 | 16.348 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8549.876 | 8660.281 | 0.992 | 0.026 | 0.043 |
# parameters of interest
params_lcs_asser_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_asser_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t3 ~ asser_t2", "d_asser_1 =~ asser_t3", "d_asser_1 ~ asser_t2", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t2 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t2 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t3 ~ asser_t2 | 1.000 | 1.000 | 1.000 | 1.000 | NA | NA |
| d_asser_1 =~ asser_t3 | 1.000 | 1.000 | 1.000 | 0.505 | NA | NA |
| d_asser_1 ~1 | 0.450 | 0.111 | 0.790 | 1.784 | 2.601 | 0.009 |
| asser_t2 ~1 | 3.174 | 3.051 | 3.298 | 6.356 | 50.321 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.059 | 0.008 | 0.111 | 0.932 | 2.255 | 0.024 |
| asser_t2 ~ frequ | 0.205 | 0.078 | 0.332 | 0.257 | 3.160 | 0.002 |
| d_asser_1 ~ asser_t2 | -0.136 | -0.243 | -0.029 | -0.269 | -2.494 | 0.013 |
| d_asser_1 ~ frequ | 0.026 | -0.055 | 0.108 | 0.065 | 0.636 | 0.525 |
| frequ ~~ frequ | 0.390 | 0.255 | 0.525 | 1.000 | 5.669 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7225.895 | 7310.539 | 0.962 | 0.057 | 0.062 |
# parameters of interest
params_lcs_energ_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t3 ~ energ_t2", "d_energ_1 =~ energ_t3", "d_energ_1 ~ energ_t2", # change parameters
"d_energ_1 ~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", "d_energ_1 ~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t2 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t3 ~ energ_t2 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_energ_1 =~ energ_t3 | 1.000 | 1.000 | 1.000 | 0.619 | NA | NA |
| d_energ_1 ~1 | 0.550 | 0.220 | 0.881 | 3.361 | 3.262 | 0.001 |
| energ_t2 ~1 | 2.450 | 2.242 | 2.658 | 9.113 | 23.072 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.023 | -0.002 | 0.049 | 0.875 | 1.783 | 0.075 |
| d_energ_1 ~ energ_t2 | -0.228 | -0.370 | -0.086 | -0.374 | -3.143 | 0.002 |
| d_energ_1 ~ sb07_03_t1 | 0.017 | -0.008 | 0.043 | 0.134 | 1.324 | 0.186 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.598 | 1.386 | 1.811 | 1.000 | 14.753 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8553.011 | 8663.416 | 0.957 | 0.056 | 0.063 |
# parameters of interest
params_lcs_energ_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_energ_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t3 ~ energ_t2", "d_energ_1 =~ energ_t3", "d_energ_1 ~ energ_t2", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t2 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t2 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t3 ~ energ_t2 | 1.000 | 1.000 | 1.000 | 1.019 | NA | NA |
| d_energ_1 =~ energ_t3 | 1.000 | 1.000 | 1.000 | 0.620 | NA | NA |
| d_energ_1 ~1 | 0.584 | 0.248 | 0.920 | 3.278 | 3.411 | 0.001 |
| energ_t2 ~1 | 2.700 | 2.587 | 2.812 | 9.222 | 46.966 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.028 | -0.003 | 0.059 | 0.882 | 1.791 | 0.073 |
| energ_t2 ~ frequ | -0.131 | -0.262 | 0.000 | -0.276 | -1.965 | 0.049 |
| d_energ_1 ~ energ_t2 | -0.217 | -0.341 | -0.094 | -0.357 | -3.446 | 0.001 |
| d_energ_1 ~ frequ | -0.023 | -0.084 | 0.038 | -0.081 | -0.751 | 0.453 |
| frequ ~~ frequ | 0.382 | 0.246 | 0.517 | 1.000 | 5.502 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7068.754 | 7153.398 | 0.947 | 0.06 | 0.062 |
# parameters of interest
params_lcs_compa_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t3 ~ compa_t2", "d_compa_1 =~ compa_t3", "d_compa_1 ~ compa_t2", # change parameters
"d_compa_1 ~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", "d_compa_1 ~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t2 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t3 ~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.915 | NA | NA |
| d_compa_1 =~ compa_t3 | 1.000 | 1.000 | 1.000 | 0.391 | NA | NA |
| d_compa_1 ~1 | 0.356 | -0.607 | 1.320 | 1.345 | 0.725 | 0.469 |
| compa_t2 ~1 | 4.540 | 4.354 | 4.726 | 7.311 | 47.955 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.055 | -0.029 | 0.138 | 0.782 | 1.289 | 0.197 |
| d_compa_1 ~ compa_t2 | -0.038 | -0.250 | 0.174 | -0.089 | -0.352 | 0.725 |
| d_compa_1 ~ sb07_04_t1 | -0.093 | -0.153 | -0.033 | -0.478 | -3.037 | 0.002 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.845 | 1.652 | 2.037 | 1.000 | 18.759 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8349.071 | 8459.476 | 0.926 | 0.068 | 0.081 |
# parameters of interest
params_lcs_compa_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_compa_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t3 ~ compa_t2", "d_compa_1 =~ compa_t3", "d_compa_1 ~ compa_t2", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t2 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t2 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t3 ~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.917 | NA | NA |
| d_compa_1 =~ compa_t3 | 1.000 | 1.000 | 1.000 | 0.371 | NA | NA |
| d_compa_1 ~1 | -0.216 | -1.307 | 0.875 | -0.806 | -0.388 | 0.698 |
| compa_t2 ~1 | 4.280 | 4.186 | 4.373 | 6.467 | 89.925 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.070 | -0.037 | 0.176 | 0.969 | 1.277 | 0.202 |
| compa_t2 ~ frequ | 0.338 | 0.159 | 0.518 | 0.327 | 3.695 | 0.000 |
| d_compa_1 ~ compa_t2 | 0.037 | -0.208 | 0.282 | 0.091 | 0.296 | 0.767 |
| d_compa_1 ~ frequ | -0.077 | -0.225 | 0.072 | -0.183 | -1.010 | 0.313 |
| frequ ~~ frequ | 0.410 | 0.269 | 0.551 | 1.000 | 5.698 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 6160.624 | 6245.268 | 0.986 | 0.034 | 0.045 |
# parameters of interest
params_lcs_respe_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t3 ~ respe_t2", "d_respe_1 =~ respe_t3", "d_respe_1 ~ respe_t2", # change parameters
"d_respe_1 ~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", "d_respe_1 ~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t2 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t3 ~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.918 | NA | NA |
| d_respe_1 =~ respe_t3 | 1.000 | 1.000 | 1.000 | 0.366 | NA | NA |
| d_respe_1 ~1 | -0.168 | -0.933 | 0.597 | -0.993 | -0.431 | 0.666 |
| respe_t2 ~1 | 4.735 | 4.628 | 4.843 | 11.148 | 86.427 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.028 | -0.006 | 0.062 | 0.985 | 1.625 | 0.104 |
| d_respe_1 ~ respe_t2 | 0.031 | -0.127 | 0.188 | 0.077 | 0.380 | 0.704 |
| d_respe_1 ~ sb07_05_t1 | 0.016 | -0.021 | 0.053 | 0.127 | 0.839 | 0.401 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.849 | 1.617 | 2.081 | 1.000 | 15.617 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 7442.348 | 7552.753 | 0.974 | 0.043 | 0.052 |
# parameters of interest
params_lcs_respe_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_respe_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t3 ~ respe_t2", "d_respe_1 =~ respe_t3", "d_respe_1 ~ respe_t2", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t2 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t2 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t3 ~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.917 | NA | NA |
| d_respe_1 =~ respe_t3 | 1.000 | 1.000 | 1.000 | 0.367 | NA | NA |
| d_respe_1 ~1 | -0.138 | -0.850 | 0.575 | -0.811 | -0.379 | 0.705 |
| respe_t2 ~1 | 4.489 | 4.420 | 4.559 | 10.600 | 127.169 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.028 | -0.006 | 0.062 | 0.964 | 1.590 | 0.112 |
| respe_t2 ~ frequ | 0.158 | 0.049 | 0.266 | 0.235 | 2.851 | 0.004 |
| d_respe_1 ~ respe_t2 | 0.032 | -0.123 | 0.187 | 0.080 | 0.403 | 0.687 |
| d_respe_1 ~ frequ | -0.051 | -0.134 | 0.031 | -0.192 | -1.216 | 0.224 |
| frequ ~~ frequ | 0.399 | 0.262 | 0.537 | 1.000 | 5.691 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7402.299 | 7486.943 | 0.965 | 0.051 | 0.053 |
# parameters of interest
params_lcs_trust_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t3 ~ trust_t2", "d_trust_1 =~ trust_t3", "d_trust_1 ~ trust_t2", # change parameters
"d_trust_1 ~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", "d_trust_1 ~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t2 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t3 ~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.960 | NA | NA |
| d_trust_1 =~ trust_t3 | 1.000 | 1.000 | 1.000 | 0.329 | NA | NA |
| d_trust_1 ~1 | 0.039 | -0.334 | 0.411 | 0.213 | 0.203 | 0.839 |
| trust_t2 ~1 | 2.585 | 2.355 | 2.815 | 4.883 | 22.023 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.033 | -0.011 | 0.076 | 0.995 | 1.467 | 0.142 |
| d_trust_1 ~ trust_t2 | -0.018 | -0.166 | 0.130 | -0.053 | -0.240 | 0.810 |
| d_trust_1 ~ sb07_06_t1 | 0.007 | -0.037 | 0.052 | 0.053 | 0.311 | 0.755 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.847 | 1.664 | 2.031 | 1.000 | 19.773 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8666.936 | 8777.341 | 0.97 | 0.044 | 0.06 |
# parameters of interest
params_lcs_trust_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_trust_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t3 ~ trust_t2", "d_trust_1 =~ trust_t3", "d_trust_1 ~ trust_t2", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t2 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t2 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t3 ~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.961 | NA | NA |
| d_trust_1 =~ trust_t3 | 1.000 | 1.000 | 1.000 | 0.332 | NA | NA |
| d_trust_1 ~1 | 0.066 | -0.342 | 0.473 | 0.384 | 0.316 | 0.752 |
| trust_t2 ~1 | 2.712 | 2.583 | 2.841 | 5.472 | 41.328 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.029 | -0.009 | 0.068 | 0.995 | 1.483 | 0.138 |
| trust_t2 ~ frequ | -0.136 | -0.280 | 0.007 | -0.173 | -1.862 | 0.063 |
| d_trust_1 ~ trust_t2 | -0.021 | -0.175 | 0.132 | -0.062 | -0.272 | 0.786 |
| d_trust_1 ~ frequ | -0.014 | -0.120 | 0.091 | -0.052 | -0.266 | 0.790 |
| frequ ~~ frequ | 0.395 | 0.258 | 0.531 | 1.000 | 5.675 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 6796.285 | 6880.929 | 0.965 | 0.07 | 0.04 |
# parameters of interest
params_lcs_organ_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t3 ~ organ_t2", "d_organ_1 =~ organ_t3", "d_organ_1 ~ organ_t2", # change parameters
"d_organ_1 ~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", "d_organ_1 ~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t2 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t3 ~ organ_t2 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_organ_1 =~ organ_t3 | 1.000 | 1.000 | 1.000 | 0.466 | NA | NA |
| d_organ_1 ~1 | 0.274 | 0.058 | 0.491 | 0.563 | 2.485 | 0.013 |
| organ_t2 ~1 | 1.388 | 1.082 | 1.694 | 1.293 | 8.881 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.218 | 0.069 | 0.368 | 0.919 | 2.865 | 0.004 |
| d_organ_1 ~ organ_t2 | -0.134 | -0.275 | 0.007 | -0.295 | -1.868 | 0.062 |
| d_organ_1 ~ sb07_07_t1 | 0.006 | -0.081 | 0.094 | 0.019 | 0.142 | 0.887 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.187 | 1.980 | 2.394 | 1.000 | 20.736 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8102.581 | 8212.986 | 0.974 | 0.051 | 0.043 |
# parameters of interest
params_lcs_organ_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_organ_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t3 ~ organ_t2", "d_organ_1 =~ organ_t3", "d_organ_1 ~ organ_t2", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t2 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t2 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t3 ~ organ_t2 | 1.000 | 1.000 | 1.000 | 1.022 | NA | NA |
| d_organ_1 =~ organ_t3 | 1.000 | 1.000 | 1.000 | 0.467 | NA | NA |
| d_organ_1 ~1 | 0.309 | 0.064 | 0.554 | 0.665 | 2.475 | 0.013 |
| organ_t2 ~1 | 2.734 | 2.583 | 2.885 | 2.685 | 35.435 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.191 | 0.057 | 0.325 | 0.882 | 2.801 | 0.005 |
| organ_t2 ~ frequ | -0.245 | -0.470 | -0.020 | -0.150 | -2.138 | 0.032 |
| d_organ_1 ~ organ_t2 | -0.140 | -0.236 | -0.044 | -0.306 | -2.851 | 0.004 |
| d_organ_1 ~ frequ | -0.155 | -0.304 | -0.005 | -0.208 | -2.030 | 0.042 |
| frequ ~~ frequ | 0.390 | 0.253 | 0.528 | 1.000 | 5.562 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 6884.26 | 6968.904 | 0.953 | 0.076 | 0.049 |
# parameters of interest
params_lcs_produ_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t3 ~ produ_t2", "d_produ_1 =~ produ_t3", "d_produ_1 ~ produ_t2", # change parameters
"d_produ_1 ~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", "d_produ_1 ~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t2 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t3 ~ produ_t2 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_produ_1 =~ produ_t3 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_produ_1 ~1 | 0.564 | 0.203 | 0.925 | 1.060 | 3.061 | 0.002 |
| produ_t2 ~1 | 1.831 | 1.450 | 2.212 | 2.009 | 9.423 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.251 | 0.102 | 0.399 | 0.885 | 3.314 | 0.001 |
| d_produ_1 ~ produ_t2 | -0.229 | -0.416 | -0.042 | -0.392 | -2.406 | 0.016 |
| d_produ_1 ~ sb07_08_t1 | 0.050 | -0.050 | 0.150 | 0.129 | 0.981 | 0.327 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.880 | 1.615 | 2.144 | 1.000 | 13.934 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8224.842 | 8335.247 | 0.946 | 0.071 | 0.051 |
# parameters of interest
params_lcs_produ_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_produ_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t3 ~ produ_t2", "d_produ_1 =~ produ_t3", "d_produ_1 ~ produ_t2", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t2 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t2 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t3 ~ produ_t2 | 1.000 | 1.000 | 1.000 | 1.011 | NA | NA |
| d_produ_1 =~ produ_t3 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_produ_1 ~1 | 0.582 | 0.159 | 1.006 | 1.117 | 2.693 | 0.007 |
| produ_t2 ~1 | 3.211 | 3.067 | 3.355 | 3.617 | 43.783 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.245 | 0.093 | 0.397 | 0.900 | 3.164 | 0.002 |
| produ_t2 ~ frequ | -0.289 | -0.513 | -0.066 | -0.204 | -2.539 | 0.011 |
| d_produ_1 ~ produ_t2 | -0.177 | -0.314 | -0.039 | -0.301 | -2.519 | 0.012 |
| d_produ_1 ~ frequ | 0.042 | -0.121 | 0.206 | 0.051 | 0.510 | 0.610 |
| frequ ~~ frequ | 0.390 | 0.251 | 0.530 | 1.000 | 5.493 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7230.617 | 7315.261 | 0.92 | 0.08 | 0.074 |
# parameters of interest
params_lcs_respo_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t3 ~ respo_t2", "d_respo_1 =~ respo_t3", "d_respo_1 ~ respo_t2", # change parameters
"d_respo_1 ~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", "d_respo_1 ~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t2 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t3 ~ respo_t2 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_respo_1 =~ respo_t3 | 1.000 | 1.000 | 1.000 | 0.684 | NA | NA |
| d_respo_1 ~1 | 1.039 | 0.347 | 1.732 | 2.945 | 2.941 | 0.003 |
| respo_t2 ~1 | 4.134 | 3.928 | 4.340 | 7.880 | 39.266 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.108 | 0.027 | 0.188 | 0.864 | 2.615 | 0.009 |
| d_respo_1 ~ respo_t2 | -0.265 | -0.426 | -0.104 | -0.394 | -3.223 | 0.001 |
| d_respo_1 ~ sb07_09_t1 | -0.019 | -0.068 | 0.030 | -0.080 | -0.753 | 0.451 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.211 | 2.012 | 2.409 | 1.000 | 21.798 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8486.343 | 8596.748 | 0.932 | 0.067 | 0.074 |
# parameters of interest
params_lcs_respo_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_respo_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t3 ~ respo_t2", "d_respo_1 =~ respo_t3", "d_respo_1 ~ respo_t2", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t2 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t2 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t3 ~ respo_t2 | 1.000 | 1.000 | 1.000 | 1.011 | NA | NA |
| d_respo_1 =~ respo_t3 | 1.000 | 1.000 | 1.000 | 0.680 | NA | NA |
| d_respo_1 ~1 | 0.880 | 0.371 | 1.389 | 2.386 | 3.390 | 0.001 |
| respo_t2 ~1 | 3.680 | 3.570 | 3.791 | 6.707 | 65.147 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.119 | 0.024 | 0.214 | 0.875 | 2.449 | 0.014 |
| respo_t2 ~ frequ | 0.071 | -0.090 | 0.233 | 0.081 | 0.862 | 0.389 |
| d_respo_1 ~ respo_t2 | -0.238 | -0.376 | -0.100 | -0.354 | -3.377 | 0.001 |
| d_respo_1 ~ frequ | 0.010 | -0.105 | 0.125 | 0.017 | 0.173 | 0.863 |
| frequ ~~ frequ | 0.394 | 0.255 | 0.532 | 1.000 | 5.562 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7295.89 | 7380.534 | 0.996 | 0.019 | 0.037 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t3 ~ anxie_t2", "d_anxie_1 =~ anxie_t3", "d_anxie_1 ~ anxie_t2", # change parameters
"d_anxie_1 ~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", "d_anxie_1 ~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t2 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t3 ~ anxie_t2 | 1.000 | 1.000 | 1.000 | 1.025 | NA | NA |
| d_anxie_1 =~ anxie_t3 | 1.000 | 1.000 | 1.000 | 0.477 | NA | NA |
| d_anxie_1 ~1 | 1.186 | 0.648 | 1.723 | 3.059 | 4.327 | 0.000 |
| anxie_t2 ~1 | 3.986 | 3.714 | 4.259 | 4.791 | 28.633 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.117 | 0.039 | 0.195 | 0.778 | 2.932 | 0.003 |
| d_anxie_1 ~ anxie_t2 | -0.234 | -0.360 | -0.108 | -0.503 | -3.639 | 0.000 |
| d_anxie_1 ~ sb07_10_t1 | -0.111 | -0.173 | -0.048 | -0.435 | -3.466 | 0.001 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.322 | 2.077 | 2.568 | 1.000 | 18.545 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8582.599 | 8693.004 | 0.976 | 0.041 | 0.048 |
# parameters of interest
params_lcs_anxie_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_anxie_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t3 ~ anxie_t2", "d_anxie_1 =~ anxie_t3", "d_anxie_1 ~ anxie_t2", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t2 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t2 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t3 ~ anxie_t2 | 1.000 | 1.000 | 1.000 | 1.033 | NA | NA |
| d_anxie_1 =~ anxie_t3 | 1.000 | 1.000 | 1.000 | 0.484 | NA | NA |
| d_anxie_1 ~1 | 0.506 | 0.197 | 0.816 | 1.290 | 3.209 | 0.001 |
| anxie_t2 ~1 | 3.001 | 2.862 | 3.139 | 3.582 | 42.380 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.139 | 0.051 | 0.228 | 0.904 | 3.084 | 0.002 |
| anxie_t2 ~ frequ | 0.021 | -0.201 | 0.242 | 0.016 | 0.183 | 0.855 |
| d_anxie_1 ~ anxie_t2 | -0.140 | -0.244 | -0.037 | -0.299 | -2.652 | 0.008 |
| d_anxie_1 ~ frequ | -0.045 | -0.197 | 0.106 | -0.073 | -0.586 | 0.558 |
| frequ ~~ frequ | 0.397 | 0.258 | 0.537 | 1.000 | 5.580 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7101.267 | 7185.911 | 0.97 | 0.065 | 0.054 |
# parameters of interest
params_lcs_depre_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t3 ~ depre_t2", "d_depre_1 =~ depre_t3", "d_depre_1 ~ depre_t2", # change parameters
"d_depre_1 ~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", "d_depre_1 ~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t2 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t3 ~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_depre_1 =~ depre_t3 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_depre_1 ~1 | 0.675 | 0.125 | 1.225 | 2.027 | 2.405 | 0.016 |
| depre_t2 ~1 | 4.064 | 3.877 | 4.252 | 6.942 | 42.396 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.102 | 0.053 | 0.152 | 0.923 | 4.029 | 0.000 |
| d_depre_1 ~ depre_t2 | -0.181 | -0.307 | -0.054 | -0.318 | -2.795 | 0.005 |
| d_depre_1 ~ sb07_11_t1 | -0.014 | -0.064 | 0.037 | -0.068 | -0.540 | 0.589 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.619 | 2.421 | 2.817 | 1.000 | 25.943 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8411.381 | 8521.786 | 0.936 | 0.08 | 0.086 |
# parameters of interest
params_lcs_depre_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_depre_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t3 ~ depre_t2", "d_depre_1 =~ depre_t3", "d_depre_1 ~ depre_t2", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t2 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t2 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t3 ~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.993 | NA | NA |
| d_depre_1 =~ depre_t3 | 1.000 | 1.000 | 1.000 | 0.561 | NA | NA |
| d_depre_1 ~1 | 0.538 | 0.285 | 0.792 | 1.597 | 4.160 | 0.000 |
| depre_t2 ~1 | 3.279 | 3.156 | 3.402 | 5.488 | 52.202 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.105 | 0.053 | 0.158 | 0.927 | 3.949 | 0.000 |
| depre_t2 ~ frequ | 0.006 | -0.144 | 0.156 | 0.006 | 0.077 | 0.939 |
| d_depre_1 ~ depre_t2 | -0.153 | -0.228 | -0.077 | -0.270 | -3.964 | 0.000 |
| d_depre_1 ~ frequ | 0.002 | -0.093 | 0.097 | 0.004 | 0.041 | 0.967 |
| frequ ~~ frequ | 0.396 | 0.255 | 0.537 | 1.000 | 5.518 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7222.467 | 7307.111 | 0.989 | 0.034 | 0.033 |
# parameters of interest
params_lcs_volat_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t3 ~ volat_t2", "d_volat_1 =~ volat_t3", "d_volat_1 ~ volat_t2", # change parameters
"d_volat_1 ~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", "d_volat_1 ~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t2 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t3 ~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_volat_1 =~ volat_t3 | 1.000 | 1.000 | 1.000 | 0.419 | NA | NA |
| d_volat_1 ~1 | 0.297 | 0.022 | 0.573 | 0.779 | 2.114 | 0.034 |
| volat_t2 ~1 | 2.306 | 2.047 | 2.565 | 2.533 | 17.459 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.137 | 0.033 | 0.241 | 0.940 | 2.583 | 0.010 |
| d_volat_1 ~ volat_t2 | -0.064 | -0.171 | 0.043 | -0.153 | -1.170 | 0.242 |
| d_volat_1 ~ sb07_12_t1 | -0.037 | -0.105 | 0.030 | -0.142 | -1.079 | 0.281 |
| sb07_12_t1 ~~ sb07_12_t1 | 2.109 | 1.902 | 2.316 | 1.000 | 19.963 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8483.733 | 8594.138 | 1 | 0 | 0.031 |
# parameters of interest
params_lcs_volat_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_volat_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t3 ~ volat_t2", "d_volat_1 =~ volat_t3", "d_volat_1 ~ volat_t2", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t2 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t2 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t3 ~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_volat_1 =~ volat_t3 | 1.000 | 1.000 | 1.000 | 0.418 | NA | NA |
| d_volat_1 ~1 | 0.260 | -0.018 | 0.539 | 0.690 | 1.835 | 0.067 |
| volat_t2 ~1 | 2.951 | 2.813 | 3.089 | 3.270 | 41.853 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.135 | 0.034 | 0.236 | 0.950 | 2.624 | 0.009 |
| volat_t2 ~ frequ | -0.017 | -0.227 | 0.192 | -0.012 | -0.164 | 0.870 |
| d_volat_1 ~ volat_t2 | -0.086 | -0.178 | 0.006 | -0.206 | -1.837 | 0.066 |
| d_volat_1 ~ frequ | -0.054 | -0.202 | 0.094 | -0.090 | -0.716 | 0.474 |
| frequ ~~ frequ | 0.395 | 0.258 | 0.532 | 1.000 | 5.643 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 6933.358 | 7018.002 | 0.994 | 0.021 | 0.037 |
# parameters of interest
params_lcs_curio_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t3 ~ curio_t2", "d_curio_1 =~ curio_t3", "d_curio_1 ~ curio_t2", # change parameters
"d_curio_1 ~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", "d_curio_1 ~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t2 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t3 ~ curio_t2 | 1.000 | 1.000 | 1.000 | 1.105 | NA | NA |
| d_curio_1 =~ curio_t3 | 1.000 | 1.000 | 1.000 | 0.421 | NA | NA |
| d_curio_1 ~1 | 0.764 | 0.165 | 1.362 | 4.364 | 2.499 | 0.012 |
| curio_t2 ~1 | 4.389 | 4.220 | 4.559 | 9.561 | 50.860 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.025 | -0.004 | 0.053 | 0.801 | 1.696 | 0.090 |
| d_curio_1 ~ curio_t2 | -0.175 | -0.308 | -0.043 | -0.459 | -2.589 | 0.010 |
| d_curio_1 ~ sb07_13_t1 | -0.017 | -0.054 | 0.020 | -0.127 | -0.904 | 0.366 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.719 | 1.512 | 1.926 | 1.000 | 16.250 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8225.646 | 8336.051 | 0.976 | 0.039 | 0.053 |
# parameters of interest
params_lcs_curio_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_curio_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t3 ~ curio_t2", "d_curio_1 =~ curio_t3", "d_curio_1 ~ curio_t2", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t2 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t2 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t3 ~ curio_t2 | 1.000 | 1.000 | 1.000 | 1.103 | NA | NA |
| d_curio_1 =~ curio_t3 | 1.000 | 1.000 | 1.000 | 0.418 | NA | NA |
| d_curio_1 ~1 | 0.648 | 0.052 | 1.244 | 3.812 | 2.133 | 0.033 |
| curio_t2 ~1 | 4.189 | 4.096 | 4.281 | 9.342 | 88.360 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.024 | -0.003 | 0.051 | 0.818 | 1.725 | 0.084 |
| curio_t2 ~ frequ | 0.180 | 0.044 | 0.316 | 0.252 | 2.600 | 0.009 |
| d_curio_1 ~ curio_t2 | -0.157 | -0.295 | -0.019 | -0.414 | -2.232 | 0.026 |
| d_curio_1 ~ frequ | -0.011 | -0.095 | 0.072 | -0.041 | -0.264 | 0.791 |
| frequ ~~ frequ | 0.394 | 0.254 | 0.534 | 1.000 | 5.499 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 7151.964 | 7236.608 | 0.984 | 0.041 | 0.044 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t3 ~ aesth_t2", "d_aesth_1 =~ aesth_t3", "d_aesth_1 ~ aesth_t2", # change parameters
"d_aesth_1 ~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", "d_aesth_1 ~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t2 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t3 ~ aesth_t2 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_aesth_1 =~ aesth_t3 | 1.000 | 1.000 | 1.000 | 0.441 | NA | NA |
| d_aesth_1 ~1 | 0.392 | 0.130 | 0.654 | 4.258 | 2.929 | 0.003 |
| aesth_t2 ~1 | 2.854 | 2.702 | 3.007 | 13.067 | 36.744 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.007 | -0.005 | 0.020 | 0.883 | 1.190 | 0.234 |
| d_aesth_1 ~ aesth_t2 | -0.133 | -0.227 | -0.039 | -0.316 | -2.781 | 0.005 |
| d_aesth_1 ~ sb07_14_t1 | -0.009 | -0.022 | 0.005 | -0.128 | -1.277 | 0.202 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.910 | 1.720 | 2.100 | 1.000 | 19.738 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 8403.449 | 8513.854 | 0.971 | 0.051 | 0.05 |
# parameters of interest
params_lcs_aesth_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_aesth_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t3 ~ aesth_t2", "d_aesth_1 =~ aesth_t3", "d_aesth_1 ~ aesth_t2", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t2 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t2 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t3 ~ aesth_t2 | 1.000 | 1.000 | 1.000 | 1.047 | NA | NA |
| d_aesth_1 =~ aesth_t3 | 1.000 | 1.000 | 1.000 | 0.440 | NA | NA |
| d_aesth_1 ~1 | 0.367 | 0.093 | 0.641 | 3.984 | 2.628 | 0.009 |
| aesth_t2 ~1 | 2.858 | 2.723 | 2.993 | 13.045 | 41.461 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.008 | -0.005 | 0.020 | 0.901 | 1.198 | 0.231 |
| aesth_t2 ~ frequ | -0.066 | -0.147 | 0.014 | -0.191 | -1.616 | 0.106 |
| d_aesth_1 ~ aesth_t2 | -0.133 | -0.230 | -0.035 | -0.316 | -2.661 | 0.008 |
| d_aesth_1 ~ frequ | -0.001 | -0.033 | 0.031 | -0.006 | -0.050 | 0.960 |
| frequ ~~ frequ | 0.395 | 0.256 | 0.534 | 1.000 | 5.579 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 23 | 6692.048 | 6776.692 | 0.95 | 0.069 | 0.063 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp6_fu <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t3 ~ imagi_t2", "d_imagi_1 =~ imagi_t3", "d_imagi_1 ~ imagi_t2", # change parameters
"d_imagi_1 ~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", "d_imagi_1 ~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t2 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t3 ~ imagi_t2 | 1.000 | 1.000 | 1.000 | 1.147 | NA | NA |
| d_imagi_1 =~ imagi_t3 | 1.000 | 1.000 | 1.000 | 0.577 | NA | NA |
| d_imagi_1 ~1 | 0.990 | 0.417 | 1.563 | 2.733 | 3.388 | 0.001 |
| imagi_t2 ~1 | 4.224 | 4.022 | 4.425 | 5.866 | 41.089 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.100 | 0.031 | 0.168 | 0.760 | 2.861 | 0.004 |
| d_imagi_1 ~ imagi_t2 | -0.248 | -0.381 | -0.115 | -0.493 | -3.661 | 0.000 |
| d_imagi_1 ~ sb07_15_t1 | -0.003 | -0.046 | 0.041 | -0.010 | -0.117 | 0.907 |
| sb07_15_t1 ~~ sb07_15_t1 | 2.008 | 1.812 | 2.203 | 1.000 | 20.155 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_frequ_hyp6_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 293 | 30 | 7938.351 | 8048.756 | 0.959 | 0.057 | 0.059 |
# parameters of interest
params_lcs_imagi_curr_frequ_hyp6_fu <- broom::tidy(fit_mi_lcs_imagi_curr_frequ_hyp6_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t3 ~ imagi_t2", "d_imagi_1 =~ imagi_t3", "d_imagi_1 ~ imagi_t2", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t2 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t2 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_frequ_hyp6_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t3 ~ imagi_t2 | 1.000 | 1.000 | 1.000 | 1.146 | NA | NA |
| d_imagi_1 =~ imagi_t3 | 1.000 | 1.000 | 1.000 | 0.578 | NA | NA |
| d_imagi_1 ~1 | 1.024 | 0.462 | 1.586 | 2.795 | 3.572 | 0.000 |
| imagi_t2 ~1 | 3.737 | 3.636 | 3.839 | 5.146 | 71.916 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.101 | 0.033 | 0.170 | 0.755 | 2.922 | 0.003 |
| imagi_t2 ~ frequ | 0.362 | 0.169 | 0.555 | 0.311 | 3.673 | 0.000 |
| d_imagi_1 ~ imagi_t2 | -0.259 | -0.401 | -0.117 | -0.513 | -3.579 | 0.000 |
| d_imagi_1 ~ frequ | 0.046 | -0.085 | 0.177 | 0.078 | 0.684 | 0.494 |
| frequ ~~ frequ | 0.390 | 0.257 | 0.523 | 1.000 | 5.731 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7936.436 | 8024.366 | 0.96 | 0.042 | 0.046 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ sb07_01_t1", "sb07_01_t1 ~~ sb07_01_t1", "d_socia_1 ~ sb07_01_t1", # change goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.624 | NA | NA |
| d_socia_1 ~1 | 0.793 | 0.024 | 1.562 | 2.934 | 2.020 | 0.043 |
| socia_t1 ~1 | 4.029 | 3.797 | 4.261 | 9.498 | 34.046 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.064 | 0.010 | 0.119 | 0.883 | 2.328 | 0.020 |
| d_socia_1 ~ socia_t1 | -0.161 | -0.342 | 0.020 | -0.252 | -1.741 | 0.082 |
| d_socia_1 ~ sb07_01_t1 | -0.041 | -0.086 | 0.005 | -0.189 | -1.743 | 0.081 |
| sb07_01_t1 ~~ sb07_01_t1 | 1.597 | 1.400 | 1.794 | 1.000 | 15.898 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9104.029 | 9218.72 | 0.953 | 0.046 | 0.056 |
# parameters of interest
params_lcs_socia_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_socia_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.982 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.626 | NA | NA |
| d_socia_1 ~1 | 0.793 | 0.022 | 1.565 | 2.875 | 2.016 | 0.044 |
| socia_t1 ~1 | 4.241 | 4.158 | 4.324 | 9.796 | 100.182 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.068 | 0.011 | 0.124 | 0.888 | 2.355 | 0.019 |
| socia_t1 ~ frequ | 0.062 | -0.064 | 0.189 | 0.091 | 0.964 | 0.335 |
| d_socia_1 ~ socia_t1 | -0.195 | -0.374 | -0.016 | -0.305 | -2.130 | 0.033 |
| d_socia_1 ~ frequ | 0.073 | -0.039 | 0.186 | 0.168 | 1.276 | 0.202 |
| frequ ~~ frequ | 0.399 | 0.261 | 0.538 | 1.000 | 5.660 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8045.558 | 8133.488 | 0.946 | 0.048 | 0.047 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ sb07_02_t1", "sb07_02_t1 ~~ sb07_02_t1", "d_asser_1 ~ sb07_02_t1", # change goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.974 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_asser_1 ~1 | 0.735 | -0.141 | 1.611 | 3.693 | 1.644 | 0.100 |
| asser_t1 ~1 | 4.114 | 3.962 | 4.266 | 13.358 | 53.180 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.036 | -0.006 | 0.079 | 0.921 | 1.678 | 0.093 |
| d_asser_1 ~ asser_t1 | -0.182 | -0.393 | 0.030 | -0.281 | -1.684 | 0.092 |
| d_asser_1 ~ sb07_02_t1 | 0.004 | -0.026 | 0.034 | 0.027 | 0.270 | 0.787 |
| sb07_02_t1 ~~ sb07_02_t1 | 1.688 | 1.496 | 1.881 | 1.000 | 17.182 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9162.303 | 9276.995 | 0.972 | 0.035 | 0.043 |
# parameters of interest
params_lcs_asser_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_asser_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.653 | NA | NA |
| d_asser_1 ~1 | 1.023 | 0.050 | 1.995 | 4.992 | 2.060 | 0.039 |
| asser_t1 ~1 | 4.145 | 4.064 | 4.226 | 13.499 | 100.349 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.037 | -0.003 | 0.077 | 0.878 | 1.805 | 0.071 |
| asser_t1 ~ frequ | 0.179 | 0.077 | 0.281 | 0.372 | 3.447 | 0.001 |
| d_asser_1 ~ asser_t1 | -0.248 | -0.480 | -0.015 | -0.371 | -2.086 | 0.037 |
| d_asser_1 ~ frequ | 0.062 | -0.020 | 0.143 | 0.192 | 1.483 | 0.138 |
| frequ ~~ frequ | 0.405 | 0.269 | 0.541 | 1.000 | 5.845 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7893.195 | 7981.125 | 0.949 | 0.039 | 0.046 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ sb07_03_t1", "sb07_03_t1 ~~ sb07_03_t1", "d_energ_1 ~ sb07_03_t1", # change goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.802 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_energ_1 ~1 | -0.078 | -0.823 | 0.667 | -0.649 | -0.205 | 0.838 |
| energ_t1 ~1 | 2.314 | 2.176 | 2.452 | 13.687 | 32.861 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.014 | -0.007 | 0.036 | 0.988 | 1.303 | 0.193 |
| d_energ_1 ~ energ_t1 | 0.036 | -0.286 | 0.358 | 0.050 | 0.218 | 0.828 |
| d_energ_1 ~ sb07_03_t1 | 0.010 | -0.014 | 0.034 | 0.103 | 0.816 | 0.414 |
| sb07_03_t1 ~~ sb07_03_t1 | 1.572 | 1.373 | 1.770 | 1.000 | 15.549 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9047.43 | 9162.121 | 0.967 | 0.034 | 0.046 |
# parameters of interest
params_lcs_energ_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_energ_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 0.813 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.551 | NA | NA |
| d_energ_1 ~1 | 0.025 | -0.742 | 0.792 | 0.207 | 0.063 | 0.949 |
| energ_t1 ~1 | 2.242 | 2.124 | 2.360 | 12.665 | 37.296 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.014 | -0.006 | 0.035 | 0.991 | 1.372 | 0.170 |
| energ_t1 ~ frequ | -0.091 | -0.171 | -0.011 | -0.328 | -2.229 | 0.026 |
| d_energ_1 ~ energ_t1 | 0.007 | -0.343 | 0.356 | 0.010 | 0.037 | 0.971 |
| d_energ_1 ~ frequ | -0.017 | -0.088 | 0.053 | -0.092 | -0.480 | 0.631 |
| frequ ~~ frequ | 0.407 | 0.268 | 0.545 | 1.000 | 5.756 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8389.855 | 8477.785 | 0.938 | 0.048 | 0.057 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ sb07_04_t1", "sb07_04_t1 ~~ sb07_04_t1", "d_compa_1 ~ sb07_04_t1", # change goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.429 | NA | NA |
| d_compa_1 ~1 | 0.083 | -1.009 | 1.176 | 0.379 | 0.150 | 0.881 |
| compa_t1 ~1 | 4.443 | 4.285 | 4.600 | 8.815 | 55.369 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.045 | -0.030 | 0.119 | 0.919 | 1.180 | 0.238 |
| d_compa_1 ~ compa_t1 | -0.053 | -0.294 | 0.189 | -0.121 | -0.429 | 0.668 |
| d_compa_1 ~ sb07_04_t1 | 0.038 | -0.015 | 0.090 | 0.232 | 1.407 | 0.159 |
| sb07_04_t1 ~~ sb07_04_t1 | 1.843 | 1.662 | 2.024 | 1.000 | 19.988 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9504.94 | 9619.631 | 0.941 | 0.048 | 0.066 |
# parameters of interest
params_lcs_compa_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_compa_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.433 | NA | NA |
| d_compa_1 ~1 | 0.315 | -0.743 | 1.374 | 1.387 | 0.584 | 0.559 |
| compa_t1 ~1 | 4.226 | 4.132 | 4.320 | 8.210 | 88.386 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.050 | -0.026 | 0.125 | 0.964 | 1.296 | 0.195 |
| compa_t1 ~ frequ | 0.194 | 0.068 | 0.319 | 0.240 | 3.021 | 0.003 |
| d_compa_1 ~ compa_t1 | -0.085 | -0.329 | 0.160 | -0.191 | -0.679 | 0.497 |
| d_compa_1 ~ frequ | 0.029 | -0.106 | 0.164 | 0.081 | 0.418 | 0.676 |
| frequ ~~ frequ | 0.406 | 0.266 | 0.546 | 1.000 | 5.672 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 6803.064 | 6890.994 | 0.966 | 0.041 | 0.046 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ sb07_05_t1", "sb07_05_t1 ~~ sb07_05_t1", "d_respe_1 ~ sb07_05_t1", # change goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.108 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.678 | NA | NA |
| d_respe_1 ~1 | 1.410 | 0.638 | 2.183 | 5.850 | 3.579 | 0.000 |
| respe_t1 ~1 | 4.797 | 4.699 | 4.895 | 12.175 | 95.990 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.045 | 0.015 | 0.075 | 0.777 | 2.961 | 0.003 |
| d_respe_1 ~ respe_t1 | -0.289 | -0.448 | -0.131 | -0.473 | -3.570 | 0.000 |
| d_respe_1 ~ sb07_05_t1 | -0.021 | -0.055 | 0.014 | -0.115 | -1.178 | 0.239 |
| sb07_05_t1 ~~ sb07_05_t1 | 1.826 | 1.607 | 2.046 | 1.000 | 16.296 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 7910.311 | 8025.003 | 0.969 | 0.039 | 0.05 |
# parameters of interest
params_lcs_respe_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respe_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 1.108 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.678 | NA | NA |
| d_respe_1 ~1 | 1.334 | 0.493 | 2.174 | 5.528 | 3.110 | 0.002 |
| respe_t1 ~1 | 4.710 | 4.650 | 4.770 | 11.948 | 153.622 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.046 | 0.015 | 0.077 | 0.791 | 2.925 | 0.003 |
| respe_t1 ~ frequ | 0.196 | 0.108 | 0.285 | 0.321 | 4.351 | 0.000 |
| d_respe_1 ~ respe_t1 | -0.283 | -0.457 | -0.110 | -0.463 | -3.196 | 0.001 |
| d_respe_1 ~ frequ | 0.007 | -0.066 | 0.079 | 0.018 | 0.180 | 0.857 |
| frequ ~~ frequ | 0.415 | 0.274 | 0.556 | 1.000 | 5.782 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8210.959 | 8298.889 | 0.957 | 0.045 | 0.047 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ sb07_06_t1", "sb07_06_t1 ~~ sb07_06_t1", "d_trust_1 ~ sb07_06_t1", # change goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.896 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.335 | NA | NA |
| d_trust_1 ~1 | 0.007 | -0.389 | 0.402 | 0.053 | 0.034 | 0.973 |
| trust_t1 ~1 | 1.915 | 1.774 | 2.055 | 5.499 | 26.727 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.016 | -0.020 | 0.052 | 0.953 | 0.883 | 0.377 |
| d_trust_1 ~ trust_t1 | 0.052 | -0.143 | 0.246 | 0.138 | 0.522 | 0.602 |
| d_trust_1 ~ sb07_06_t1 | -0.016 | -0.052 | 0.020 | -0.164 | -0.863 | 0.388 |
| sb07_06_t1 ~~ sb07_06_t1 | 1.829 | 1.657 | 2.000 | 1.000 | 20.861 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9319.462 | 9434.154 | 0.974 | 0.035 | 0.046 |
# parameters of interest
params_lcs_trust_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_trust_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 0.898 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.333 | NA | NA |
| d_trust_1 ~1 | -0.076 | -0.472 | 0.320 | -0.593 | -0.377 | 0.706 |
| trust_t1 ~1 | 1.900 | 1.808 | 1.993 | 5.490 | 40.160 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.015 | -0.020 | 0.051 | 0.938 | 0.848 | 0.397 |
| trust_t1 ~ frequ | -0.151 | -0.249 | -0.053 | -0.275 | -3.015 | 0.003 |
| d_trust_1 ~ trust_t1 | 0.073 | -0.138 | 0.283 | 0.196 | 0.676 | 0.499 |
| d_trust_1 ~ frequ | 0.044 | -0.054 | 0.142 | 0.217 | 0.886 | 0.376 |
| frequ ~~ frequ | 0.397 | 0.260 | 0.533 | 1.000 | 5.701 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7316.953 | 7404.883 | 0.762 | 0.086 | 0.092 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ sb07_07_t1", "sb07_07_t1 ~~ sb07_07_t1", "d_organ_1 ~ sb07_07_t1", # change goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.978 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.720 | NA | NA |
| d_organ_1 ~1 | 0.312 | 0.001 | 0.623 | 1.109 | 1.969 | 0.049 |
| organ_t1 ~1 | 1.711 | 1.535 | 1.886 | 4.477 | 19.129 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.069 | 0.015 | 0.123 | 0.870 | 2.517 | 0.012 |
| d_organ_1 ~ organ_t1 | -0.244 | -0.441 | -0.048 | -0.332 | -2.439 | 0.015 |
| d_organ_1 ~ sb07_07_t1 | 0.025 | -0.015 | 0.065 | 0.130 | 1.214 | 0.225 |
| sb07_07_t1 ~~ sb07_07_t1 | 2.198 | 2.006 | 2.391 | 1.000 | 22.374 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 8354.305 | 8468.996 | 0.871 | 0.068 | 0.081 |
# parameters of interest
params_lcs_organ_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_organ_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.712 | NA | NA |
| d_organ_1 ~1 | 0.476 | 0.168 | 0.785 | 1.729 | 3.028 | 0.002 |
| organ_t1 ~1 | 1.679 | 1.576 | 1.782 | 4.404 | 31.924 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.062 | 0.015 | 0.110 | 0.819 | 2.567 | 0.010 |
| organ_t1 ~ frequ | -0.156 | -0.257 | -0.054 | -0.264 | -2.993 | 0.003 |
| d_organ_1 ~ organ_t1 | -0.296 | -0.493 | -0.100 | -0.410 | -2.952 | 0.003 |
| d_organ_1 ~ frequ | -0.113 | -0.218 | -0.009 | -0.266 | -2.126 | 0.034 |
| frequ ~~ frequ | 0.418 | 0.278 | 0.559 | 1.000 | 5.841 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 6176.632 | 6264.562 | 0.916 | 0.071 | 0.06 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ sb07_08_t1", "sb07_08_t1 ~~ sb07_08_t1", "d_produ_1 ~ sb07_08_t1", # change goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.883 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.578 | NA | NA |
| d_produ_1 ~1 | 0.013 | -0.349 | 0.375 | 0.051 | 0.069 | 0.945 |
| produ_t1 ~1 | 1.680 | 1.531 | 1.830 | 4.390 | 21.987 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.060 | 0.008 | 0.112 | 0.965 | 2.279 | 0.023 |
| d_produ_1 ~ produ_t1 | -0.065 | -0.269 | 0.139 | -0.100 | -0.627 | 0.531 |
| d_produ_1 ~ sb07_08_t1 | 0.027 | -0.004 | 0.059 | 0.151 | 1.699 | 0.089 |
| sb07_08_t1 ~~ sb07_08_t1 | 1.899 | 1.651 | 2.148 | 1.000 | 14.992 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 7274.847 | 7389.538 | 0.932 | 0.062 | 0.06 |
# parameters of interest
params_lcs_produ_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_produ_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.885 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.580 | NA | NA |
| d_produ_1 ~1 | 0.157 | -0.153 | 0.468 | 0.630 | 0.995 | 0.320 |
| produ_t1 ~1 | 1.596 | 1.517 | 1.675 | 4.188 | 39.495 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.061 | 0.009 | 0.113 | 0.974 | 2.291 | 0.022 |
| produ_t1 ~ frequ | -0.121 | -0.206 | -0.035 | -0.201 | -2.754 | 0.006 |
| d_produ_1 ~ produ_t1 | -0.091 | -0.299 | 0.116 | -0.139 | -0.863 | 0.388 |
| d_produ_1 ~ frequ | -0.045 | -0.122 | 0.032 | -0.115 | -1.141 | 0.254 |
| frequ ~~ frequ | 0.405 | 0.265 | 0.545 | 1.000 | 5.678 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7839.417 | 7927.347 | 0.935 | 0.055 | 0.051 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ sb07_09_t1", "sb07_09_t1 ~~ sb07_09_t1", "d_respo_1 ~ sb07_09_t1", # change goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.019 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.664 | NA | NA |
| d_respo_1 ~1 | 0.974 | 0.215 | 1.732 | 5.091 | 2.515 | 0.012 |
| respo_t1 ~1 | 4.211 | 4.072 | 4.350 | 14.351 | 59.248 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.032 | 0.002 | 0.062 | 0.874 | 2.093 | 0.036 |
| d_respo_1 ~ respo_t1 | -0.232 | -0.408 | -0.055 | -0.356 | -2.572 | 0.010 |
| d_respo_1 ~ sb07_09_t1 | -0.003 | -0.029 | 0.023 | -0.024 | -0.231 | 0.818 |
| sb07_09_t1 ~~ sb07_09_t1 | 2.198 | 2.013 | 2.382 | 1.000 | 23.300 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 8879.191 | 8993.882 | 0.94 | 0.053 | 0.056 |
# parameters of interest
params_lcs_respo_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_respo_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.022 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.667 | NA | NA |
| d_respo_1 ~1 | 1.054 | 0.275 | 1.832 | 5.507 | 2.652 | 0.008 |
| respo_t1 ~1 | 4.188 | 4.079 | 4.297 | 14.292 | 75.481 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.032 | 0.002 | 0.062 | 0.863 | 2.071 | 0.038 |
| respo_t1 ~ frequ | 0.137 | 0.039 | 0.235 | 0.301 | 2.731 | 0.006 |
| d_respo_1 ~ respo_t1 | -0.253 | -0.436 | -0.070 | -0.387 | -2.714 | 0.007 |
| d_respo_1 ~ frequ | 0.028 | -0.034 | 0.091 | 0.096 | 0.889 | 0.374 |
| frequ ~~ frequ | 0.414 | 0.271 | 0.558 | 1.000 | 5.669 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7993.568 | 8081.498 | 0.949 | 0.031 | 0.047 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ sb07_10_t1", "sb07_10_t1 ~~ sb07_10_t1", "d_anxie_1 ~ sb07_10_t1", # change goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.892 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.874 | NA | NA |
| d_anxie_1 ~1 | 1.595 | 0.125 | 3.066 | 6.203 | 2.127 | 0.033 |
| anxie_t1 ~1 | 4.506 | 4.370 | 4.641 | 17.174 | 65.163 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.058 | 0.007 | 0.108 | 0.871 | 2.228 | 0.026 |
| d_anxie_1 ~ anxie_t1 | -0.354 | -0.668 | -0.040 | -0.361 | -2.207 | 0.027 |
| d_anxie_1 ~ sb07_10_t1 | 0.003 | -0.025 | 0.031 | 0.017 | 0.202 | 0.840 |
| sb07_10_t1 ~~ sb07_10_t1 | 2.275 | 2.040 | 2.510 | 1.000 | 18.986 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9033.768 | 9148.459 | 0.938 | 0.042 | 0.056 |
# parameters of interest
params_lcs_anxie_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_anxie_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.885 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.879 | NA | NA |
| d_anxie_1 ~1 | 1.652 | 0.066 | 3.238 | 6.085 | 2.041 | 0.041 |
| anxie_t1 ~1 | 4.597 | 4.535 | 4.659 | 16.819 | 145.508 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.064 | 0.009 | 0.120 | 0.871 | 2.260 | 0.024 |
| anxie_t1 ~ frequ | 0.110 | 0.007 | 0.212 | 0.258 | 2.102 | 0.036 |
| d_anxie_1 ~ anxie_t1 | -0.364 | -0.707 | -0.022 | -0.367 | -2.088 | 0.037 |
| d_anxie_1 ~ frequ | 0.015 | -0.079 | 0.109 | 0.035 | 0.311 | 0.756 |
| frequ ~~ frequ | 0.413 | 0.266 | 0.560 | 1.000 | 5.513 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 6793.041 | 6880.971 | 0.899 | 0.069 | 0.066 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ sb07_11_t1", "sb07_11_t1 ~~ sb07_11_t1", "d_depre_1 ~ sb07_11_t1", # change goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.885 | NA | NA |
| d_depre_1 ~1 | 1.783 | 0.837 | 2.729 | 7.163 | 3.693 | 0.000 |
| depre_t1 ~1 | 4.440 | 4.326 | 4.554 | 15.239 | 76.575 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.048 | 0.013 | 0.084 | 0.779 | 2.673 | 0.008 |
| d_depre_1 ~ depre_t1 | -0.398 | -0.606 | -0.189 | -0.466 | -3.734 | 0.000 |
| d_depre_1 ~ sb07_11_t1 | -0.009 | -0.030 | 0.012 | -0.058 | -0.841 | 0.400 |
| sb07_11_t1 ~~ sb07_11_t1 | 2.609 | 2.418 | 2.799 | 1.000 | 26.880 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 7788.956 | 7903.648 | 0.905 | 0.067 | 0.075 |
# parameters of interest
params_lcs_depre_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_depre_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.036 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.885 | NA | NA |
| d_depre_1 ~1 | 1.770 | 0.823 | 2.716 | 7.038 | 3.666 | 0.000 |
| depre_t1 ~1 | 4.455 | 4.384 | 4.525 | 15.138 | 124.689 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.049 | 0.013 | 0.086 | 0.781 | 2.656 | 0.008 |
| depre_t1 ~ frequ | 0.059 | -0.027 | 0.145 | 0.128 | 1.353 | 0.176 |
| d_depre_1 ~ depre_t1 | -0.401 | -0.610 | -0.193 | -0.470 | -3.769 | 0.000 |
| d_depre_1 ~ frequ | 0.008 | -0.051 | 0.066 | 0.020 | 0.262 | 0.793 |
| frequ ~~ frequ | 0.403 | 0.262 | 0.545 | 1.000 | 5.596 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7504.069 | 7591.999 | 0.97 | 0.035 | 0.042 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ sb07_12_t1", "sb07_12_t1 ~~ sb07_12_t1", "d_volat_1 ~ sb07_12_t1", # change goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.842 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.636 | NA | NA |
| d_volat_1 ~1 | 0.094 | -0.253 | 0.441 | 0.297 | 0.530 | 0.596 |
| volat_t1 ~1 | 1.647 | 1.508 | 1.786 | 3.934 | 23.236 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.097 | 0.021 | 0.174 | 0.975 | 2.492 | 0.013 |
| d_volat_1 ~ volat_t1 | -0.074 | -0.297 | 0.148 | -0.098 | -0.655 | 0.513 |
| d_volat_1 ~ sb07_12_t1 | 0.026 | -0.015 | 0.066 | 0.118 | 1.243 | 0.214 |
| sb07_12_t1 ~~ sb07_12_t1 | 2.104 | 1.911 | 2.296 | 1.000 | 21.424 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 8568.034 | 8682.725 | 0.961 | 0.041 | 0.053 |
# parameters of interest
params_lcs_volat_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_volat_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 0.841 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.635 | NA | NA |
| d_volat_1 ~1 | 0.196 | -0.156 | 0.548 | 0.621 | 1.090 | 0.276 |
| volat_t1 ~1 | 1.599 | 1.523 | 1.674 | 3.825 | 41.336 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.097 | 0.021 | 0.173 | 0.979 | 2.506 | 0.012 |
| volat_t1 ~ frequ | -0.131 | -0.231 | -0.032 | -0.202 | -2.591 | 0.010 |
| d_volat_1 ~ volat_t1 | -0.094 | -0.326 | 0.138 | -0.125 | -0.795 | 0.427 |
| d_volat_1 ~ frequ | -0.051 | -0.162 | 0.060 | -0.104 | -0.905 | 0.366 |
| frequ ~~ frequ | 0.412 | 0.267 | 0.557 | 1.000 | 5.568 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8112.716 | 8200.646 | 0.954 | 0.041 | 0.052 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ sb07_13_t1", "sb07_13_t1 ~~ sb07_13_t1", "d_curio_1 ~ sb07_13_t1", # change goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.980 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.469 | NA | NA |
| d_curio_1 ~1 | 0.420 | -0.540 | 1.380 | 2.971 | 0.857 | 0.391 |
| curio_t1 ~1 | 4.592 | 4.489 | 4.695 | 15.562 | 87.584 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.019 | -0.006 | 0.044 | 0.957 | 1.504 | 0.133 |
| d_curio_1 ~ curio_t1 | -0.091 | -0.297 | 0.115 | -0.190 | -0.865 | 0.387 |
| d_curio_1 ~ sb07_13_t1 | 0.007 | -0.023 | 0.037 | 0.064 | 0.447 | 0.655 |
| sb07_13_t1 ~~ sb07_13_t1 | 1.720 | 1.527 | 1.912 | 1.000 | 17.527 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9247.911 | 9362.602 | 0.95 | 0.045 | 0.059 |
# parameters of interest
params_lcs_curio_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_curio_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 0.977 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.459 | NA | NA |
| d_curio_1 ~1 | 0.468 | -0.464 | 1.400 | 3.246 | 0.983 | 0.325 |
| curio_t1 ~1 | 4.536 | 4.465 | 4.607 | 14.808 | 125.815 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.018 | -0.008 | 0.045 | 0.891 | 1.356 | 0.175 |
| curio_t1 ~ frequ | 0.042 | -0.064 | 0.148 | 0.086 | 0.772 | 0.440 |
| d_curio_1 ~ curio_t1 | -0.098 | -0.300 | 0.104 | -0.208 | -0.948 | 0.343 |
| d_curio_1 ~ frequ | 0.063 | -0.014 | 0.140 | 0.276 | 1.604 | 0.109 |
| frequ ~~ frequ | 0.397 | 0.258 | 0.535 | 1.000 | 5.607 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 8276.038 | 8363.968 | 0.989 | 0.027 | 0.045 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ sb07_14_t1", "sb07_14_t1 ~~ sb07_14_t1", "d_aesth_1 ~ sb07_14_t1", # change goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_aesth_1 ~1 | 0.415 | -0.128 | 0.958 | 30.818 | 1.499 | 0.134 |
| aesth_t1 ~1 | 3.055 | 2.919 | 3.192 | 124.403 | 43.825 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | 0.939 | 2.341 | 0.019 |
| d_aesth_1 ~ aesth_t1 | -0.135 | -0.313 | 0.042 | -0.247 | -1.497 | 0.134 |
| d_aesth_1 ~ sb07_14_t1 | 0.000 | -0.002 | 0.002 | 0.000 | 0.003 | 0.998 |
| sb07_14_t1 ~~ sb07_14_t1 | 1.960 | 1.782 | 2.138 | 1.000 | 21.634 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 9371.926 | 9486.617 | 0.981 | 0.033 | 0.051 |
# parameters of interest
params_lcs_aesth_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_aesth_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_aesth_1 ~1 | 0.417 | -0.109 | 0.944 | 37.075 | 1.554 | 0.120 |
| aesth_t1 ~1 | 3.064 | 2.927 | 3.201 | 149.123 | 43.851 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.000 | 0.000 | 0.000 | 0.939 | 2.329 | 0.020 |
| aesth_t1 ~ frequ | 0.007 | 0.002 | 0.012 | 0.207 | 2.739 | 0.006 |
| d_aesth_1 ~ aesth_t1 | -0.136 | -0.307 | 0.035 | -0.249 | -1.561 | 0.119 |
| d_aesth_1 ~ frequ | 0.000 | -0.004 | 0.004 | 0.007 | 0.069 | 0.945 |
| frequ ~~ frequ | 0.398 | 0.257 | 0.539 | 1.000 | 5.528 | 0.000 |
Results summary (sb07_xx_t1 = trait/facet specific change goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 23 | 7480.097 | 7568.027 | 0.889 | 0.067 | 0.066 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp6 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ sb07_15_t1", "sb07_15_t1 ~~ sb07_15_t1", "d_imagi_1 ~ sb07_15_t1", # change goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.068 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.740 | NA | NA |
| d_imagi_1 ~1 | 1.433 | 0.367 | 2.499 | 5.454 | 2.635 | 0.008 |
| imagi_t1 ~1 | 4.613 | 4.482 | 4.744 | 12.166 | 68.960 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.056 | 0.020 | 0.092 | 0.809 | 3.059 | 0.002 |
| d_imagi_1 ~ imagi_t1 | -0.302 | -0.526 | -0.078 | -0.435 | -2.639 | 0.008 |
| d_imagi_1 ~ sb07_15_t1 | -0.007 | -0.038 | 0.025 | -0.037 | -0.427 | 0.669 |
| sb07_15_t1 ~~ sb07_15_t1 | 2.018 | 1.837 | 2.198 | 1.000 | 21.903 | 0.000 |
Results summary (frequ = frequency of self-improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_frequ_hyp6) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 338 | 30 | 8555.027 | 8669.719 | 0.92 | 0.058 | 0.074 |
# parameters of interest
params_lcs_imagi_ideal_frequ_hyp6 <- broom::tidy(fit_mi_lcs_imagi_ideal_frequ_hyp6, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_frequ_hyp6, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.064 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.746 | NA | NA |
| d_imagi_1 ~1 | 1.505 | 0.354 | 2.656 | 5.801 | 2.563 | 0.010 |
| imagi_t1 ~1 | 4.612 | 4.550 | 4.673 | 12.468 | 146.635 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.054 | 0.020 | 0.088 | 0.799 | 3.119 | 0.002 |
| imagi_t1 ~ frequ | 0.126 | 0.031 | 0.221 | 0.216 | 2.609 | 0.009 |
| d_imagi_1 ~ imagi_t1 | -0.322 | -0.568 | -0.076 | -0.459 | -2.562 | 0.010 |
| d_imagi_1 ~ frequ | 0.047 | -0.060 | 0.155 | 0.116 | 0.863 | 0.388 |
| frequ ~~ frequ | 0.401 | 0.261 | 0.541 | 1.000 | 5.622 | 0.000 |
Collect all relevant effects
# create df for table:
# main effects (change score)
df_table_hyp6_main <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~1 "),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~1 "),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~1 "),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~1 ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_main = estimate, std_main = std.all, p_main = p.value)
# moderation effects
df_table_hyp6_mod <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_mod = estimate, std_mod = std.all, p_mod = p.value)
# combine
df_table_hyp6 <- df_table_hyp6_main %>% left_join(df_table_hyp6_mod)Results summary across the Big Five traits: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6[1:10, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| extraversion | goals | 0.906 | 3.114 | 0.003 | -0.090 | -0.231 | 0.320 |
| extraversion | frequency | 0.683 | 2.353 | 0.000 | 0.132 | 0.285 | 0.002 |
| agreeableness | goals | 0.672 | 3.306 | 0.001 | 0.003 | 0.015 | 0.895 |
| agreeableness | frequency | 0.810 | 3.773 | 0.000 | 0.061 | 0.181 | 0.083 |
| conscientiousness | goals | 0.789 | 1.902 | 0.001 | -0.058 | -0.169 | 0.149 |
| conscientiousness | frequency | 0.593 | 1.443 | 0.000 | 0.067 | 0.102 | 0.217 |
| neuroticism | goals | 0.806 | 1.747 | 0.000 | 0.090 | 0.222 | 0.053 |
| neuroticism | frequency | 0.528 | 1.145 | 0.000 | -0.048 | -0.066 | 0.376 |
| openness | goals | 0.461 | 1.717 | 0.013 | -0.020 | -0.060 | 0.558 |
| openness | frequency | 0.495 | 1.870 | 0.006 | 0.072 | 0.169 | 0.108 |
Results summary across the Big Five facets: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6[11:40, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| sociability | goals | 0.795 | 2.026 | 0.001 | -0.036 | -0.116 | 0.280 |
| sociability | frequency | 0.547 | 1.439 | 0.000 | 0.191 | 0.319 | 0.001 |
| assertiveness | goals | 0.669 | 2.406 | 0.000 | -0.033 | -0.153 | 0.090 |
| assertiveness | frequency | 0.528 | 1.960 | 0.001 | 0.063 | 0.146 | 0.155 |
| energy | goals | 0.374 | 2.473 | 0.012 | -0.027 | -0.220 | 0.066 |
| energy | frequency | 0.464 | 2.948 | 0.001 | -0.081 | -0.317 | 0.016 |
| compassion | goals | 0.795 | 3.204 | 0.005 | 0.019 | 0.106 | 0.405 |
| compassion | frequency | 1.006 | 4.012 | 0.000 | 0.075 | 0.189 | 0.214 |
| respectfulness | goals | 1.399 | 4.481 | 0.000 | -0.019 | -0.081 | 0.362 |
| respectfulness | frequency | 1.243 | 3.977 | 0.001 | -0.026 | -0.053 | 0.593 |
| trust | goals | 0.463 | 1.476 | 0.007 | -0.032 | -0.138 | 0.226 |
| trust | frequency | 0.421 | 1.443 | 0.010 | -0.022 | -0.047 | 0.693 |
| organization | goals | 0.224 | 0.457 | 0.064 | -0.001 | -0.003 | 0.979 |
| organization | frequency | 0.250 | 0.544 | 0.056 | -0.054 | -0.074 | 0.450 |
| productiveness | goals | 0.551 | 0.908 | 0.005 | 0.071 | 0.162 | 0.113 |
| productiveness | frequency | 0.656 | 1.095 | 0.002 | -0.159 | -0.166 | 0.054 |
| responsibility | goals | 0.764 | 3.007 | 0.006 | -0.008 | -0.044 | 0.694 |
| responsibility | frequency | 0.708 | 2.722 | 0.000 | 0.010 | 0.024 | 0.829 |
| anxiety | goals | 0.009 | 0.022 | 0.979 | 0.085 | 0.301 | 0.050 |
| anxiety | frequency | 0.581 | 1.344 | 0.000 | 0.059 | 0.086 | 0.467 |
| depression | goals | 1.192 | 3.303 | 0.000 | -0.052 | -0.234 | 0.091 |
| depression | frequency | 0.744 | 2.000 | 0.000 | 0.034 | 0.057 | 0.496 |
| volatility | goals | 0.679 | 1.187 | 0.000 | 0.048 | 0.121 | 0.144 |
| volatility | frequency | 0.730 | 1.289 | 0.000 | 0.028 | 0.031 | 0.720 |
| curiosity | goals | 0.148 | 0.768 | 0.694 | 0.038 | 0.257 | 0.075 |
| curiosity | frequency | 0.472 | 2.467 | 0.124 | 0.121 | 0.397 | 0.007 |
| aesthetic | goals | 0.340 | 3.438 | 0.018 | 0.002 | 0.023 | 0.800 |
| aesthetic | frequency | 0.341 | 3.407 | 0.026 | 0.000 | 0.001 | 0.996 |
| imagination | goals | 0.684 | 2.021 | 0.015 | -0.018 | -0.075 | 0.487 |
| imagination | frequency | 0.691 | 1.982 | 0.002 | 0.114 | 0.205 | 0.076 |
Prepare data frame for plotting:
df_table_hyp6_plot <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_curr_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_curr_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_curr_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_curr_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_curr_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_curr_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_curr_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_curr_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_curr_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_curr_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_curr_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_curr_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_curr_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_curr_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_curr_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp6_plot <- df_table_hyp6_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("current", levels = c("current"), labels = c("current"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses (only showing the moderation effects):
ggplot(df_table_hyp6_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Run models for all facets with a template & loop:
# create df for table:
# main effects (change score)
df_table_hyp6_ideal_main <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp6 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_extra_ideal_frequ_hyp6 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_agree_ideal_specif_hyp6 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_agree_ideal_frequ_hyp6 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_consc_ideal_specif_hyp6 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_consc_ideal_frequ_hyp6 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_neuro_ideal_specif_hyp6 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_neuro_ideal_frequ_hyp6 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_openn_ideal_specif_hyp6 %>% filter(term=="d_openn_1 ~1 "),
params_lcs_openn_ideal_frequ_hyp6 %>% filter(term=="d_openn_1 ~1 "),
#facets
params_lcs_socia_ideal_specif_hyp6 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_socia_ideal_frequ_hyp6 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_asser_ideal_specif_hyp6 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_asser_ideal_frequ_hyp6 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_energ_ideal_specif_hyp6 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_energ_ideal_frequ_hyp6 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_compa_ideal_specif_hyp6 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_compa_ideal_frequ_hyp6 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_respe_ideal_specif_hyp6 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_respe_ideal_frequ_hyp6 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_trust_ideal_specif_hyp6 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_trust_ideal_frequ_hyp6 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_organ_ideal_specif_hyp6 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_organ_ideal_frequ_hyp6 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_produ_ideal_specif_hyp6 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_produ_ideal_frequ_hyp6 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_respo_ideal_specif_hyp6 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_respo_ideal_frequ_hyp6 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_anxie_ideal_specif_hyp6 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_anxie_ideal_frequ_hyp6 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_depre_ideal_specif_hyp6 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_depre_ideal_frequ_hyp6 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_volat_ideal_specif_hyp6 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_volat_ideal_frequ_hyp6 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_curio_ideal_specif_hyp6 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_curio_ideal_frequ_hyp6 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_aesth_ideal_specif_hyp6 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_aesth_ideal_frequ_hyp6 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_imagi_ideal_specif_hyp6 %>% filter(term=="d_imagi_1 ~1 "),
params_lcs_imagi_ideal_frequ_hyp6 %>% filter(term=="d_imagi_1 ~1 ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_main = estimate, std_main = std.all, p_main = p.value)
# moderation effects
df_table_hyp6_ideal_mod <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_ideal_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_ideal_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_ideal_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_ideal_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_ideal_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_ideal_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_ideal_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_ideal_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_ideal_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_ideal_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_ideal_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_ideal_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_mod = estimate, std_mod = std.all, p_mod = p.value)
# combine
df_table_hyp6_ideal <- df_table_hyp6_ideal_main %>% left_join(df_table_hyp6_ideal_mod)Results summary across the Big Five traits: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6_ideal[1:10, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| extraversion | goals | 0.668 | 2.786 | 0.011 | 0.039 | 0.093 | 0.587 |
| extraversion | frequency | 0.784 | 3.334 | 0.004 | 0.073 | 0.199 | 0.082 |
| agreeableness | goals | 0.863 | 3.158 | 0.000 | 0.001 | 0.004 | 0.969 |
| agreeableness | frequency | 0.875 | 3.262 | 0.000 | 0.005 | 0.011 | 0.914 |
| conscientiousness | goals | 0.642 | 2.418 | 0.072 | -0.028 | -0.124 | 0.173 |
| conscientiousness | frequency | 0.746 | 2.805 | 0.050 | 0.059 | 0.142 | 0.129 |
| neuroticism | goals | 0.463 | 1.270 | 0.002 | 0.031 | 0.094 | 0.246 |
| neuroticism | frequency | 0.498 | 1.367 | 0.002 | -0.041 | -0.072 | 0.432 |
| openness | goals | 0.612 | 4.146 | 0.014 | -0.002 | -0.009 | 0.927 |
| openness | frequency | 0.647 | 4.323 | 0.011 | 0.021 | 0.087 | 0.438 |
Results summary across the Big Five facets: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6_ideal[11:40, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| sociability | goals | 0.793 | 2.934 | 0.043 | -0.041 | -0.189 | 0.081 |
| sociability | frequency | 0.793 | 2.875 | 0.044 | 0.073 | 0.168 | 0.202 |
| assertiveness | goals | 0.735 | 3.693 | 0.100 | 0.004 | 0.027 | 0.787 |
| assertiveness | frequency | 1.023 | 4.992 | 0.039 | 0.062 | 0.192 | 0.138 |
| energy | goals | -0.078 | -0.649 | 0.838 | 0.010 | 0.103 | 0.414 |
| energy | frequency | 0.025 | 0.207 | 0.949 | -0.017 | -0.092 | 0.631 |
| compassion | goals | 0.083 | 0.379 | 0.881 | 0.038 | 0.232 | 0.159 |
| compassion | frequency | 0.315 | 1.387 | 0.559 | 0.029 | 0.081 | 0.676 |
| respectfulness | goals | 1.410 | 5.850 | 0.000 | -0.021 | -0.115 | 0.239 |
| respectfulness | frequency | 1.334 | 5.528 | 0.002 | 0.007 | 0.018 | 0.857 |
| trust | goals | 0.007 | 0.053 | 0.973 | -0.016 | -0.164 | 0.388 |
| trust | frequency | -0.076 | -0.593 | 0.706 | 0.044 | 0.217 | 0.376 |
| organization | goals | 0.312 | 1.109 | 0.049 | 0.025 | 0.130 | 0.225 |
| organization | frequency | 0.476 | 1.729 | 0.002 | -0.113 | -0.266 | 0.034 |
| productiveness | goals | 0.013 | 0.051 | 0.945 | 0.027 | 0.151 | 0.089 |
| productiveness | frequency | 0.157 | 0.630 | 0.320 | -0.045 | -0.115 | 0.254 |
| responsibility | goals | 0.974 | 5.091 | 0.012 | -0.003 | -0.024 | 0.818 |
| responsibility | frequency | 1.054 | 5.507 | 0.008 | 0.028 | 0.096 | 0.374 |
| anxiety | goals | 1.595 | 6.203 | 0.033 | 0.003 | 0.017 | 0.840 |
| anxiety | frequency | 1.652 | 6.085 | 0.041 | 0.015 | 0.035 | 0.756 |
| depression | goals | 1.783 | 7.163 | 0.000 | -0.009 | -0.058 | 0.400 |
| depression | frequency | 1.770 | 7.038 | 0.000 | 0.008 | 0.020 | 0.793 |
| volatility | goals | 0.094 | 0.297 | 0.596 | 0.026 | 0.118 | 0.214 |
| volatility | frequency | 0.196 | 0.621 | 0.276 | -0.051 | -0.104 | 0.366 |
| curiosity | goals | 0.420 | 2.971 | 0.391 | 0.007 | 0.064 | 0.655 |
| curiosity | frequency | 0.468 | 3.246 | 0.325 | 0.063 | 0.276 | 0.109 |
| aesthetic | goals | 0.415 | 30.818 | 0.134 | 0.000 | 0.000 | 0.998 |
| aesthetic | frequency | 0.417 | 37.075 | 0.120 | 0.000 | 0.007 | 0.945 |
| imagination | goals | 1.433 | 5.454 | 0.008 | -0.007 | -0.037 | 0.669 |
| imagination | frequency | 1.505 | 5.801 | 0.010 | 0.047 | 0.116 | 0.388 |
Prepare data frame for plotting:
df_table_hyp6_plot_ideal <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp6 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp6 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp6 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp6 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp6 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp6 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp6 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp6 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp6 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp6 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp6 %>% filter(term=="d_socia_1 ~ sb07_01_t1"),
params_lcs_socia_ideal_frequ_hyp6 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp6 %>% filter(term=="d_asser_1 ~ sb07_02_t1"),
params_lcs_asser_ideal_frequ_hyp6 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp6 %>% filter(term=="d_energ_1 ~ sb07_03_t1"),
params_lcs_energ_ideal_frequ_hyp6 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp6 %>% filter(term=="d_compa_1 ~ sb07_04_t1"),
params_lcs_compa_ideal_frequ_hyp6 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp6 %>% filter(term=="d_respe_1 ~ sb07_05_t1"),
params_lcs_respe_ideal_frequ_hyp6 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp6 %>% filter(term=="d_trust_1 ~ sb07_06_t1"),
params_lcs_trust_ideal_frequ_hyp6 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp6 %>% filter(term=="d_organ_1 ~ sb07_07_t1"),
params_lcs_organ_ideal_frequ_hyp6 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp6 %>% filter(term=="d_produ_1 ~ sb07_08_t1"),
params_lcs_produ_ideal_frequ_hyp6 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp6 %>% filter(term=="d_respo_1 ~ sb07_09_t1"),
params_lcs_respo_ideal_frequ_hyp6 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp6 %>% filter(term=="d_anxie_1 ~ sb07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp6 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp6 %>% filter(term=="d_depre_1 ~ sb07_11_t1"),
params_lcs_depre_ideal_frequ_hyp6 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp6 %>% filter(term=="d_volat_1 ~ sb07_12_t1"),
params_lcs_volat_ideal_frequ_hyp6 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp6 %>% filter(term=="d_curio_1 ~ sb07_13_t1"),
params_lcs_curio_ideal_frequ_hyp6 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp6 %>% filter(term=="d_aesth_1 ~ sb07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp6 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp6 %>% filter(term=="d_imagi_1 ~ sb07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp6 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp6_plot_ideal <- df_table_hyp6_plot_ideal %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("ideal", levels = c("ideal"), labels = c("ideal"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses (only showing the moderation effects):
ggplot(df_table_hyp6_plot_ideal, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
In the self-acceptance group, change in ideal-self ratings but not change in current-self ratings will be moderated by acceptance goals (both general and trait-/facet-specific goals) and the frequency of self-acceptance behaviors.
We will test this one domain/facet at a time. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and specify acceptance goals (or the frequency of self-acceptance behaviors) as a moderator of latent trait change. We will check in a second set of models whether changes in ideal-self ratings persist from T2 to T3.
Reshape and split data set by intervention group:
# from T! to T2
df_sbsa2_wide_pers_sa_mod <- df_sbsa2 %>%
filter(rando=="Self-Acceptance" & time %in% c(1,2)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07", # facet-specific acceptance goals
"sa04"))) %>% # frequency self-acceptance behaviors
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sa07", "sa04")))) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2,
sa04_01_t1, sa04_02_t1, sa04_03_t1)) # frequency of self-acceptance behaviors measured at T2
# colnames(df_sbsa2_wide_pers_sa_mod)
group_assign <- df_sbsa2 %>% select(pid, rando) %>% unique()
df_sbsa2_wide_pers_sa_mod <- df_sbsa2_wide_pers %>% left_join(group_assign) %>%
filter(rando=="Self-Acceptance") %>% select(-rando, -ends_with("_t3")) %>%
left_join(df_sbsa2_wide_pers_sa_mod)
# follow-up, from T2 to T3
df_sbsa2_wide_pers_sa_mod_fu <- df_sbsa2 %>%
filter(rando=="Self-Acceptance" & time %in% c(1,2)) %>%
arrange(pid, time) %>%
select(pid, time, starts_with(c("sa07", # facet-specific change goals -> asked at T1
"sa04"))) %>% # frequency self-improvement behaviors -> asked at T2
pivot_wider(names_from = time,
names_sep = "_t",
values_from = c(starts_with(c("sa07", "sa04")))) %>%
select(-c(sa07_01_t2, sa07_02_t2, sa07_03_t2, sa07_04_t2, sa07_05_t2,
sa07_06_t2, sa07_07_t2, sa07_08_t2, sa07_09_t2, sa07_10_t2,
sa07_11_t2, sa07_12_t2, sa07_13_t2, sa07_14_t2, sa07_15_t2,
sa04_01_t1, sa04_02_t1, sa04_03_t1)) # frequency of self-improvement behaviors measured at T2
# colnames(df_sbsa2_wide_pers_sa_mod_fu)
df_sbsa2_wide_pers_sa_mod_fu <- df_sbsa2_wide_pers %>% left_join(group_assign) %>%
filter(rando=="Self-Acceptance" & !is.na(valid_t2)) %>% select(-rando, -ends_with("_t1")) %>%
left_join(df_sbsa2_wide_pers_sa_mod_fu)Run models for all traits with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goals
trait_template_mod_goal_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ goals # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
trait_facets_nrs <- list(a1 = c(1:3), b2 = c(4:6), c3 = c(7:9), d4 = c(10:12), e5 = c(13:15)) # matching facet nrs to traits
# loop across 5 traits -> change in ideal self
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
mod_names = paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2], "ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), trait_model_fit))
}
# loop across 5 traits -> change in current self
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
mod_names = paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"), # current instead of ideal
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2], "ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp7")), template_filled)) # current instead of ideal
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp7")), trait_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for ideal self ratings)
trait_template_mod_goal_accept_fu <- '
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2
trait_t3 =~ 1*ind01_t3 + lamb2*ind02_t3 + lamb3*ind03_t3 # This specifies the measurement model for trait_t3 with the equality constrained factor loadings
goals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderator
trait_t3 ~ 1*trait_t2 # This parameter regresses trait_t3 perfectly on trait_t2
d_trait_1 =~ 1*trait_t3 # This defines the latent change score factor as measured perfectly by scores on trait_t3
trait_t3 ~ 0*1 # This line constrains the intercept of trait_t3 to 0
trait_t3 ~~ 0*trait_t3 # This fixes the variance of trait_t3 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t2 ~ 1 # This estimates the intercept of trait_t2
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t2 ~~ trait_t2 # This estimates the variance of trait_t2
trait_t2 ~ goals # This estimates the moderation effect on personality at T2
d_trait_1 ~ trait_t2 + goals # This estimates the self-feedback parameter and the moderation effect on the change score
goals ~ 0*1 # This fixes the intercept of the moderator to 0
goals ~~ goals # This estimates the variance of the moderator
ind01_t2 ~~ ind01_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind02_t2 ~~ ind02_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind03_t2 ~~ ind03_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t3 ~~ res1*ind01_t3 # This allows residual variance on indicator X1 at T3
ind02_t3 ~~ res2*ind02_t3 # This allows residual variance on indicator X2 at T3
ind03_t3 ~~ res3*ind03_t3 # This allows residual variance on indicator X3 at T3
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind01_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind02_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind03_t3 ~ m3*1 # This estimates the intercept of X3 at T3
ind_goal_1 ~~ ind_goal_1
ind_goal_2 ~~ ind_goal_2
ind_goal_3 ~~ ind_goal_3
ind_goal_1 ~ 1
ind_goal_2 ~ 1
ind_goal_3 ~ 1
'
# loop across 5 traits -> change in ideal self (from T2 to T3)
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal (6 = ideal)
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
mod_names = paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad = "0"), "_t1")
template_filled <- str_replace_all(trait_template_mod_goal_accept_fu,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3"),
"ind_goal_1" = mod_names[1], "ind_goal_2" = mod_names[2], "ind_goal_3" = mod_names[3]))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7_fu")), trait_model_fit))
}
# 2nd, for frequency of self-acceptance behavior
trait_template_mod_frequ_accept <- '
trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadings
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
trait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1
d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2
trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0
trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t1 ~ 1 # This estimates the intercept of trait_t1
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1
trait_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1
ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1
ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 5 traits -> change in ideal self
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), trait_model_fit))
}
# loop across 5 traits -> change in current self
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ_accept,
c("trait" = short_name,
"ind01" = paste0(short_name, "_curr_par1"), # current instead of ideal
"ind02" = paste0(short_name, "_curr_par2"),
"ind03" = paste0(short_name, "_curr_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp7")), template_filled)) # current instead of ideal
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp7")), trait_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for current self ratings)
trait_template_mod_frequ_accept_fu <- '
trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2
trait_t3 =~ 1*ind01_t3 + lamb2*ind02_t3 + lamb3*ind03_t3 # This specifies the measurement model for extra_t3 with the equality constrained factor loadings
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
trait_t3 ~ 1*trait_t2 # This parameter regresses trait_t3 perfectly on trait_t2
d_trait_1 =~ 1*trait_t3 # This defines the latent change score factor as measured perfectly by scores on trait_t3
trait_t3 ~ 0*1 # This line constrains the intercept of trait_t3 to 0
trait_t3 ~~ 0*trait_t3 # This fixes the variance of trait_t3 to 0
d_trait_1 ~ 1 # This estimates the intercept of the change score
trait_t2 ~ 1 # This estimates the intercept of trait_t2
d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores
trait_t2 ~~ trait_t2 # This estimates the variance of trait_t2
trait_t2 ~ frequ # This estimates the moderation effect on personality at T2
d_trait_1 ~ trait_t2 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind01_t2 ~~ ind01_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind02_t2 ~~ ind02_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind03_t2 ~~ ind03_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2
ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2
ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2
ind01_t3 ~~ res1*ind01_t3 # This allows residual variance on indicator X1 at T3
ind02_t3 ~~ res2*ind02_t3 # This allows residual variance on indicator X2 at T3
ind03_t3 ~~ res3*ind03_t3 # This allows residual variance on indicator X3 at T3
ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind01_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind02_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind03_t3 ~ m3*1 # This estimates the intercept of X3 at T3
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 5 traits -> change in ideal self (from T2 to T3)
for (i in 1:5) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead!
template_filled <- str_replace_all(trait_template_mod_frequ_accept_fu,
c("trait" = short_name,
"ind01" = paste0(short_name, "_ideal_par1"),
"ind02" = paste0(short_name, "_ideal_par2"),
"ind03" = paste0(short_name, "_ideal_par3")))
trait_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7_fu")), trait_model_fit))
} Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6351.424 | 6450.669 | 0.995 | 0.018 | 0.041 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.636 | NA | NA |
| d_extra_1 ~1 | 0.674 | 0.077 | 1.271 | 2.594 | 2.214 | 0.027 |
| extra_t1 ~1 | 3.689 | 3.625 | 3.753 | 9.175 | 113.322 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.061 | 0.023 | 0.099 | 0.906 | 3.172 | 0.002 |
| extra_t1 ~ goals | 0.047 | -0.039 | 0.133 | 0.102 | 1.069 | 0.285 |
| d_extra_1 ~ extra_t1 | -0.188 | -0.348 | -0.028 | -0.291 | -2.305 | 0.021 |
| d_extra_1 ~ goals | -0.022 | -0.087 | 0.043 | -0.073 | -0.658 | 0.511 |
| goals ~~ goals | 0.757 | 0.374 | 1.139 | 1.000 | 3.874 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5346.201 | 5445.446 | 0.988 | 0.031 | 0.043 |
# parameters of interest
params_lcs_extra_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_extra_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.635 | NA | NA |
| d_extra_1 ~1 | 0.791 | 0.167 | 1.415 | 3.078 | 2.485 | 0.013 |
| extra_t1 ~1 | 3.689 | 3.625 | 3.752 | 9.238 | 113.309 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.059 | 0.022 | 0.096 | 0.894 | 3.153 | 0.002 |
| extra_t1 ~ frequ | 0.163 | 0.067 | 0.259 | 0.303 | 3.336 | 0.001 |
| d_extra_1 ~ extra_t1 | -0.219 | -0.386 | -0.053 | -0.341 | -2.578 | 0.010 |
| d_extra_1 ~ frequ | 0.045 | -0.033 | 0.123 | 0.129 | 1.129 | 0.259 |
| frequ ~~ frequ | 0.549 | 0.370 | 0.728 | 1.000 | 6.023 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6066.273 | 6165.518 | 0.989 | 0.038 | 0.04 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_agree_1 ~1 | 0.789 | 0.313 | 1.264 | 2.857 | 3.248 | 0.001 |
| agree_t1 ~1 | 3.778 | 3.705 | 3.851 | 8.422 | 101.648 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.068 | 0.040 | 0.095 | 0.886 | 4.766 | 0.000 |
| agree_t1 ~ goals | -0.050 | -0.111 | 0.011 | -0.106 | -1.610 | 0.107 |
| d_agree_1 ~ agree_t1 | -0.207 | -0.329 | -0.085 | -0.337 | -3.322 | 0.001 |
| d_agree_1 ~ goals | -0.022 | -0.076 | 0.032 | -0.076 | -0.808 | 0.419 |
| goals ~~ goals | 0.895 | 0.578 | 1.212 | 1.000 | 5.539 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5211.403 | 5310.648 | 0.993 | 0.029 | 0.036 |
# parameters of interest
params_lcs_agree_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_agree_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 1.012 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.622 | NA | NA |
| d_agree_1 ~1 | 0.860 | 0.361 | 1.359 | 3.146 | 3.378 | 0.001 |
| agree_t1 ~1 | 3.778 | 3.705 | 3.851 | 8.498 | 101.706 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.065 | 0.038 | 0.091 | 0.867 | 4.773 | 0.000 |
| agree_t1 ~ frequ | 0.148 | 0.056 | 0.240 | 0.245 | 3.163 | 0.002 |
| d_agree_1 ~ agree_t1 | -0.226 | -0.354 | -0.098 | -0.367 | -3.456 | 0.001 |
| d_agree_1 ~ frequ | 0.062 | -0.009 | 0.133 | 0.168 | 1.724 | 0.085 |
| frequ ~~ frequ | 0.542 | 0.362 | 0.721 | 1.000 | 5.908 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5703.585 | 5802.83 | 1 | 0 | 0.03 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.034 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.741 | NA | NA |
| d_consc_1 ~1 | 1.296 | 0.698 | 1.894 | 4.110 | 4.247 | 0.000 |
| consc_t1 ~1 | 4.339 | 4.279 | 4.400 | 9.858 | 141.189 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.081 | 0.048 | 0.114 | 0.816 | 4.805 | 0.000 |
| consc_t1 ~ goals | -0.014 | -0.067 | 0.040 | -0.033 | -0.504 | 0.615 |
| d_consc_1 ~ consc_t1 | -0.293 | -0.427 | -0.159 | -0.409 | -4.288 | 0.000 |
| d_consc_1 ~ goals | -0.043 | -0.087 | 0.001 | -0.145 | -1.904 | 0.057 |
| goals ~~ goals | 1.133 | 0.835 | 1.431 | 1.000 | 7.460 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 4797.333 | 4896.578 | 0.992 | 0.031 | 0.029 |
# parameters of interest
params_lcs_consc_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_consc_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 1.034 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.740 | NA | NA |
| d_consc_1 ~1 | 1.369 | 0.775 | 1.963 | 4.335 | 4.517 | 0.000 |
| consc_t1 ~1 | 4.339 | 4.279 | 4.400 | 9.838 | 141.177 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.080 | 0.047 | 0.113 | 0.800 | 4.757 | 0.000 |
| consc_t1 ~ frequ | 0.089 | -0.008 | 0.186 | 0.148 | 1.801 | 0.072 |
| d_consc_1 ~ consc_t1 | -0.309 | -0.442 | -0.176 | -0.432 | -4.556 | 0.000 |
| d_consc_1 ~ frequ | 0.084 | 0.020 | 0.148 | 0.195 | 2.581 | 0.010 |
| frequ ~~ frequ | 0.537 | 0.358 | 0.716 | 1.000 | 5.879 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5929.674 | 6028.919 | 0.99 | 0.034 | 0.045 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.066 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.699 | NA | NA |
| d_neuro_1 ~1 | 0.475 | 0.259 | 0.690 | 1.357 | 4.310 | 0.000 |
| neuro_t1 ~1 | 1.751 | 1.685 | 1.818 | 3.281 | 51.766 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.101 | 0.057 | 0.144 | 0.824 | 4.555 | 0.000 |
| neuro_t1 ~ goals | 0.035 | -0.034 | 0.104 | 0.064 | 0.995 | 0.320 |
| d_neuro_1 ~ neuro_t1 | -0.275 | -0.407 | -0.142 | -0.419 | -4.060 | 0.000 |
| d_neuro_1 ~ goals | 0.000 | -0.053 | 0.053 | -0.001 | -0.009 | 0.993 |
| goals ~~ goals | 0.949 | 0.631 | 1.267 | 1.000 | 5.852 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5014.139 | 5113.384 | 0.997 | 0.019 | 0.036 |
# parameters of interest
params_lcs_neuro_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_neuro_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 1.065 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.698 | NA | NA |
| d_neuro_1 ~1 | 0.481 | 0.264 | 0.698 | 1.368 | 4.349 | 0.000 |
| neuro_t1 ~1 | 1.751 | 1.685 | 1.818 | 3.263 | 51.737 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.102 | 0.058 | 0.146 | 0.824 | 4.541 | 0.000 |
| neuro_t1 ~ frequ | -0.151 | -0.264 | -0.039 | -0.209 | -2.644 | 0.008 |
| d_neuro_1 ~ neuro_t1 | -0.278 | -0.411 | -0.145 | -0.425 | -4.100 | 0.000 |
| d_neuro_1 ~ frequ | -0.015 | -0.096 | 0.066 | -0.032 | -0.370 | 0.711 |
| frequ ~~ frequ | 0.546 | 0.366 | 0.727 | 1.000 | 5.940 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5972.654 | 6071.899 | 0.989 | 0.037 | 0.033 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.048 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.730 | NA | NA |
| d_openn_1 ~1 | 1.229 | 0.709 | 1.748 | 5.729 | 4.637 | 0.000 |
| openn_t1 ~1 | 3.907 | 3.848 | 3.966 | 12.686 | 130.383 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.038 | 0.017 | 0.058 | 0.823 | 3.592 | 0.000 |
| openn_t1 ~ goals | -0.147 | -0.207 | -0.088 | -0.370 | -4.840 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.311 | -0.442 | -0.179 | -0.446 | -4.630 | 0.000 |
| d_openn_1 ~ goals | -0.025 | -0.066 | 0.015 | -0.092 | -1.230 | 0.219 |
| goals ~~ goals | 0.599 | 0.384 | 0.814 | 1.000 | 5.457 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5289.278 | 5388.523 | 0.991 | 0.03 | 0.038 |
# parameters of interest
params_lcs_openn_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_openn_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.047 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.731 | NA | NA |
| d_openn_1 ~1 | 1.175 | 0.684 | 1.666 | 5.437 | 4.689 | 0.000 |
| openn_t1 ~1 | 3.907 | 3.848 | 3.966 | 12.636 | 130.274 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.038 | 0.018 | 0.059 | 0.822 | 3.625 | 0.000 |
| openn_t1 ~ frequ | 0.060 | -0.003 | 0.122 | 0.142 | 1.878 | 0.060 |
| d_openn_1 ~ openn_t1 | -0.297 | -0.421 | -0.173 | -0.425 | -4.694 | 0.000 |
| d_openn_1 ~ frequ | 0.027 | -0.020 | 0.074 | 0.093 | 1.133 | 0.257 |
| frequ ~~ frequ | 0.545 | 0.366 | 0.723 | 1.000 | 5.968 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 5406.123 | 5500.996 | 1 | 0 | 0.035 |
# parameters of interest
params_lcs_extra_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_extra_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t3 ~ extra_t2", "d_extra_1 =~ extra_t3", "d_extra_1 ~ extra_t2", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t2 ~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t2 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t3 ~ extra_t2 | 1.000 | 1.000 | 1.000 | 1.033 | NA | NA |
| d_extra_1 =~ extra_t3 | 1.000 | 1.000 | 1.000 | 0.539 | NA | NA |
| d_extra_1 ~1 | 0.614 | 0.139 | 1.089 | 2.883 | 2.535 | 0.011 |
| extra_t2 ~1 | 3.650 | 3.577 | 3.724 | 8.950 | 97.362 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.040 | 0.014 | 0.066 | 0.877 | 2.991 | 0.003 |
| extra_t2 ~ goals | 0.015 | -0.073 | 0.102 | 0.031 | 0.326 | 0.744 |
| d_extra_1 ~ extra_t2 | -0.170 | -0.299 | -0.041 | -0.326 | -2.587 | 0.010 |
| d_extra_1 ~ goals | 0.035 | -0.038 | 0.108 | 0.142 | 0.941 | 0.347 |
| goals ~~ goals | 0.745 | 0.329 | 1.161 | 1.000 | 3.508 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 4903.142 | 4998.016 | 0.994 | 0.023 | 0.039 |
# parameters of interest
params_lcs_extra_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_extra_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t3 ~ extra_t2", "d_extra_1 =~ extra_t3", "d_extra_1 ~ extra_t2", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t2 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t2 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t3 ~ extra_t2 | 1.000 | 1.000 | 1.000 | 1.030 | NA | NA |
| d_extra_1 =~ extra_t3 | 1.000 | 1.000 | 1.000 | 0.542 | NA | NA |
| d_extra_1 ~1 | 0.613 | 0.085 | 1.142 | 2.912 | 2.275 | 0.023 |
| extra_t2 ~1 | 3.650 | 3.577 | 3.724 | 9.117 | 97.559 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.040 | 0.014 | 0.065 | 0.899 | 3.058 | 0.002 |
| extra_t2 ~ frequ | 0.171 | 0.076 | 0.266 | 0.314 | 3.518 | 0.000 |
| d_extra_1 ~ extra_t2 | -0.170 | -0.314 | -0.026 | -0.323 | -2.319 | 0.020 |
| d_extra_1 ~ frequ | 0.005 | -0.068 | 0.078 | 0.018 | 0.140 | 0.888 |
| frequ ~~ frequ | 0.542 | 0.365 | 0.720 | 1.000 | 5.999 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 5014.761 | 5109.635 | 0.997 | 0.022 | 0.037 |
# parameters of interest
params_lcs_agree_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_agree_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t3 ~ agree_t2", "d_agree_1 =~ agree_t3", "d_agree_1 ~ agree_t2", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t2 ~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t2 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t3 ~ agree_t2 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_agree_1 =~ agree_t3 | 1.000 | 1.000 | 1.000 | 0.480 | NA | NA |
| d_agree_1 ~1 | 0.493 | 0.118 | 0.869 | 2.223 | 2.574 | 0.010 |
| agree_t2 ~1 | 3.814 | 3.736 | 3.892 | 8.043 | 96.133 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.045 | 0.015 | 0.075 | 0.915 | 2.897 | 0.004 |
| agree_t2 ~ goals | -0.088 | -0.161 | -0.014 | -0.176 | -2.337 | 0.019 |
| d_agree_1 ~ agree_t2 | -0.130 | -0.226 | -0.033 | -0.277 | -2.626 | 0.009 |
| d_agree_1 ~ goals | 0.013 | -0.032 | 0.057 | 0.055 | 0.569 | 0.569 |
| goals ~~ goals | 0.904 | 0.560 | 1.247 | 1.000 | 5.155 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 4670.112 | 4764.986 | 0.995 | 0.027 | 0.036 |
# parameters of interest
params_lcs_agree_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_agree_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t3 ~ agree_t2", "d_agree_1 =~ agree_t3", "d_agree_1 ~ agree_t2", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t2 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t2 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t3 ~ agree_t2 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_agree_1 =~ agree_t3 | 1.000 | 1.000 | 1.000 | 0.481 | NA | NA |
| d_agree_1 ~1 | 0.438 | 0.032 | 0.845 | 1.993 | 2.112 | 0.035 |
| agree_t2 ~1 | 3.814 | 3.736 | 3.892 | 8.124 | 96.192 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.043 | 0.014 | 0.073 | 0.898 | 2.888 | 0.004 |
| agree_t2 ~ frequ | 0.183 | 0.094 | 0.273 | 0.287 | 4.018 | 0.000 |
| d_agree_1 ~ agree_t2 | -0.115 | -0.219 | -0.012 | -0.246 | -2.182 | 0.029 |
| d_agree_1 ~ frequ | -0.043 | -0.108 | 0.022 | -0.145 | -1.308 | 0.191 |
| frequ ~~ frequ | 0.541 | 0.362 | 0.720 | 1.000 | 5.933 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 4711.962 | 4806.835 | 0.978 | 0.056 | 0.038 |
# parameters of interest
params_lcs_consc_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_consc_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t3 ~ consc_t2", "d_consc_1 =~ consc_t3", "d_consc_1 ~ consc_t2", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t2 ~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t2 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t3 ~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.942 | NA | NA |
| d_consc_1 =~ consc_t3 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_consc_1 ~1 | 0.265 | -0.324 | 0.853 | 1.155 | 0.881 | 0.378 |
| consc_t2 ~1 | 4.365 | 4.297 | 4.433 | 9.629 | 126.254 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.052 | 0.020 | 0.083 | 0.984 | 3.239 | 0.001 |
| consc_t2 ~ goals | -0.056 | -0.115 | 0.003 | -0.131 | -1.850 | 0.064 |
| d_consc_1 ~ consc_t2 | -0.064 | -0.194 | 0.066 | -0.126 | -0.959 | 0.337 |
| d_consc_1 ~ goals | 0.002 | -0.045 | 0.049 | 0.009 | 0.077 | 0.938 |
| goals ~~ goals | 1.122 | 0.799 | 1.446 | 1.000 | 6.797 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 4295.346 | 4390.219 | 0.973 | 0.062 | 0.039 |
# parameters of interest
params_lcs_consc_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_consc_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t3 ~ consc_t2", "d_consc_1 =~ consc_t3", "d_consc_1 ~ consc_t2", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t2 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t2 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t3 ~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.940 | NA | NA |
| d_consc_1 =~ consc_t3 | 1.000 | 1.000 | 1.000 | 0.474 | NA | NA |
| d_consc_1 ~1 | 0.121 | -0.506 | 0.749 | 0.530 | 0.379 | 0.704 |
| consc_t2 ~1 | 4.365 | 4.297 | 4.433 | 9.605 | 126.263 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.049 | 0.019 | 0.080 | 0.935 | 3.146 | 0.002 |
| consc_t2 ~ frequ | 0.158 | 0.059 | 0.258 | 0.255 | 3.110 | 0.002 |
| d_consc_1 ~ consc_t2 | -0.031 | -0.170 | 0.107 | -0.062 | -0.441 | 0.659 |
| d_consc_1 ~ frequ | -0.072 | -0.145 | 0.000 | -0.231 | -1.960 | 0.050 |
| frequ ~~ frequ | 0.536 | 0.358 | 0.715 | 1.000 | 5.897 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 5022.118 | 5116.991 | 0.993 | 0.028 | 0.034 |
# parameters of interest
params_lcs_neuro_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_neuro_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t3 ~ neuro_t2", "d_neuro_1 =~ neuro_t3", "d_neuro_1 ~ neuro_t2", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t2 ~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t2 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t3 ~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.992 | NA | NA |
| d_neuro_1 =~ neuro_t3 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_neuro_1 ~1 | 0.211 | 0.004 | 0.418 | 0.887 | 1.994 | 0.046 |
| neuro_t2 ~1 | 1.747 | 1.676 | 1.819 | 3.531 | 48.088 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.054 | 0.012 | 0.095 | 0.950 | 2.555 | 0.011 |
| neuro_t2 ~ goals | 0.027 | -0.041 | 0.095 | 0.050 | 0.769 | 0.442 |
| d_neuro_1 ~ neuro_t2 | -0.107 | -0.231 | 0.016 | -0.224 | -1.703 | 0.089 |
| d_neuro_1 ~ goals | 0.006 | -0.047 | 0.060 | 0.025 | 0.237 | 0.812 |
| goals ~~ goals | 0.865 | 0.521 | 1.209 | 1.000 | 4.932 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 4600.913 | 4695.786 | 0.992 | 0.03 | 0.032 |
# parameters of interest
params_lcs_neuro_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_neuro_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t3 ~ neuro_t2", "d_neuro_1 =~ neuro_t3", "d_neuro_1 ~ neuro_t2", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t2 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t2 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t3 ~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_neuro_1 =~ neuro_t3 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_neuro_1 ~1 | 0.175 | -0.035 | 0.384 | 0.733 | 1.636 | 0.102 |
| neuro_t2 ~1 | 1.747 | 1.676 | 1.819 | 3.514 | 48.024 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.051 | 0.009 | 0.093 | 0.895 | 2.400 | 0.016 |
| neuro_t2 ~ frequ | -0.118 | -0.215 | -0.021 | -0.174 | -2.377 | 0.017 |
| d_neuro_1 ~ neuro_t2 | -0.086 | -0.213 | 0.040 | -0.180 | -1.339 | 0.181 |
| d_neuro_1 ~ frequ | 0.078 | 0.002 | 0.154 | 0.240 | 2.014 | 0.044 |
| frequ ~~ frequ | 0.539 | 0.361 | 0.716 | 1.000 | 5.941 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 5101.72 | 5196.593 | 1 | 0 | 0.027 |
# parameters of interest
params_lcs_openn_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_openn_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t3 ~ openn_t2", "d_openn_1 =~ openn_t3", "d_openn_1 ~ openn_t2", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t2 ~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t2 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t3 ~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.948 | NA | NA |
| d_openn_1 =~ openn_t3 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_openn_1 ~1 | 0.531 | -0.175 | 1.237 | 3.346 | 1.475 | 0.140 |
| openn_t2 ~1 | 3.856 | 3.795 | 3.918 | 14.493 | 122.448 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.024 | 0.008 | 0.040 | 0.954 | 2.979 | 0.003 |
| openn_t2 ~ goals | -0.118 | -0.169 | -0.066 | -0.343 | -4.497 | 0.000 |
| d_openn_1 ~ openn_t2 | -0.136 | -0.317 | 0.044 | -0.229 | -1.486 | 0.137 |
| d_openn_1 ~ goals | -0.015 | -0.056 | 0.026 | -0.074 | -0.723 | 0.470 |
| goals ~~ goals | 0.602 | 0.365 | 0.840 | 1.000 | 4.971 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 26 | 4867.833 | 4962.706 | 1 | 0.005 | 0.035 |
# parameters of interest
params_lcs_openn_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_openn_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t3 ~ openn_t2", "d_openn_1 =~ openn_t3", "d_openn_1 ~ openn_t2", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t2 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t2 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t3 ~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.949 | NA | NA |
| d_openn_1 =~ openn_t3 | 1.000 | 1.000 | 1.000 | 0.569 | NA | NA |
| d_openn_1 ~1 | 0.508 | -0.171 | 1.186 | 3.143 | 1.467 | 0.142 |
| openn_t2 ~1 | 3.856 | 3.795 | 3.918 | 14.314 | 122.309 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.025 | 0.009 | 0.041 | 0.954 | 3.014 | 0.003 |
| openn_t2 ~ frequ | 0.065 | 0.012 | 0.119 | 0.179 | 2.389 | 0.017 |
| d_openn_1 ~ openn_t2 | -0.130 | -0.303 | 0.042 | -0.217 | -1.481 | 0.138 |
| d_openn_1 ~ frequ | 0.012 | -0.032 | 0.055 | 0.053 | 0.520 | 0.603 |
| frequ ~~ frequ | 0.544 | 0.366 | 0.723 | 1.000 | 5.981 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6803.171 | 6902.416 | 0.974 | 0.062 | 0.043 |
# parameters of interest
params_lcs_extra_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_extra_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ goals", "goals ~~ goals", "extra_t1 ~ goals", # acceptance goals
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.027 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.451 | NA | NA |
| d_extra_1 ~1 | 0.510 | 0.191 | 0.828 | 1.902 | 3.139 | 0.002 |
| extra_t1 ~1 | 3.030 | 2.942 | 3.118 | 4.972 | 67.579 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.066 | 0.034 | 0.098 | 0.920 | 4.010 | 0.000 |
| extra_t1 ~ goals | -0.390 | -0.524 | -0.256 | -0.620 | -5.703 | 0.000 |
| d_extra_1 ~ extra_t1 | -0.138 | -0.241 | -0.034 | -0.313 | -2.606 | 0.009 |
| d_extra_1 ~ goals | -0.015 | -0.097 | 0.067 | -0.055 | -0.365 | 0.715 |
| goals ~~ goals | 0.938 | 0.600 | 1.277 | 1.000 | 5.431 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_extra_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5889.596 | 5988.841 | 0.994 | 0.03 | 0.034 |
# parameters of interest
params_lcs_extra_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_extra_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("extra_t2 ~ extra_t1", "d_extra_1 =~ extra_t2", "d_extra_1 ~ extra_t1", # change parameters
"d_extra_1 ~ frequ", "frequ ~~ frequ", "extra_t1 ~ frequ", # frequency of self improvement
"d_extra_1 ~1 ", "extra_t1 ~1 ", "", # means
"d_extra_1 ~~ d_extra_1"))
kable(params_lcs_extra_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| extra_t2 ~ extra_t1 | 1.000 | 1.000 | 1.000 | 1.026 | NA | NA |
| d_extra_1 =~ extra_t2 | 1.000 | 1.000 | 1.000 | 0.450 | NA | NA |
| d_extra_1 ~1 | 0.470 | 0.252 | 0.687 | 1.802 | 4.232 | 0.000 |
| extra_t1 ~1 | 3.032 | 2.944 | 3.119 | 5.096 | 67.775 | 0.000 |
| d_extra_1 ~~ d_extra_1 | 0.062 | 0.030 | 0.093 | 0.907 | 3.854 | 0.000 |
| extra_t1 ~ frequ | 0.063 | -0.069 | 0.195 | 0.078 | 0.938 | 0.348 |
| d_extra_1 ~ extra_t1 | -0.126 | -0.196 | -0.055 | -0.286 | -3.497 | 0.000 |
| d_extra_1 ~ frequ | 0.046 | -0.026 | 0.118 | 0.130 | 1.252 | 0.211 |
| frequ ~~ frequ | 0.540 | 0.361 | 0.720 | 1.000 | 5.893 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6043.077 | 6142.322 | 0.988 | 0.037 | 0.029 |
# parameters of interest
params_lcs_agree_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_agree_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ goals", "goals ~~ goals", "agree_t1 ~ goals", # acceptance goals
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.967 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.524 | NA | NA |
| d_agree_1 ~1 | 0.550 | 0.073 | 1.028 | 2.733 | 2.258 | 0.024 |
| agree_t1 ~1 | 3.462 | 3.398 | 3.525 | 9.309 | 107.614 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.037 | 0.012 | 0.061 | 0.902 | 2.932 | 0.003 |
| agree_t1 ~ goals | -0.115 | -0.178 | -0.052 | -0.298 | -3.580 | 0.000 |
| d_agree_1 ~ agree_t1 | -0.152 | -0.290 | -0.014 | -0.281 | -2.164 | 0.030 |
| d_agree_1 ~ goals | -0.051 | -0.105 | 0.002 | -0.247 | -1.878 | 0.060 |
| goals ~~ goals | 0.932 | 0.655 | 1.209 | 1.000 | 6.591 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_agree_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5232.872 | 5332.117 | 0.995 | 0.024 | 0.041 |
# parameters of interest
params_lcs_agree_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_agree_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("agree_t2 ~ agree_t1", "d_agree_1 =~ agree_t2", "d_agree_1 ~ agree_t1", # change parameters
"d_agree_1 ~ frequ", "frequ ~~ frequ", "agree_t1 ~ frequ", # frequency of self improvement
"d_agree_1 ~1 ", "agree_t1 ~1 ", "", # means
"d_agree_1 ~~ d_agree_1"))
kable(params_lcs_agree_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| agree_t2 ~ agree_t1 | 1.000 | 1.000 | 1.000 | 0.963 | NA | NA |
| d_agree_1 =~ agree_t2 | 1.000 | 1.000 | 1.000 | 0.522 | NA | NA |
| d_agree_1 ~1 | 0.421 | -0.015 | 0.858 | 2.051 | 1.891 | 0.059 |
| agree_t1 ~1 | 3.461 | 3.398 | 3.524 | 9.130 | 107.741 | 0.000 |
| d_agree_1 ~~ d_agree_1 | 0.040 | 0.009 | 0.070 | 0.944 | 2.567 | 0.010 |
| agree_t1 ~ frequ | 0.047 | -0.036 | 0.129 | 0.090 | 1.112 | 0.266 |
| d_agree_1 ~ agree_t1 | -0.114 | -0.239 | 0.011 | -0.211 | -1.792 | 0.073 |
| d_agree_1 ~ frequ | 0.036 | -0.021 | 0.092 | 0.128 | 1.243 | 0.214 |
| frequ ~~ frequ | 0.539 | 0.362 | 0.717 | 1.000 | 5.949 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6265.051 | 6364.296 | 0.98 | 0.064 | 0.028 |
# parameters of interest
params_lcs_consc_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_consc_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ goals", "goals ~~ goals", "consc_t1 ~ goals", # acceptance goals
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.512 | NA | NA |
| d_consc_1 ~1 | 0.578 | 0.208 | 0.947 | 1.660 | 3.067 | 0.002 |
| consc_t1 ~1 | 3.380 | 3.299 | 3.461 | 5.053 | 81.588 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.114 | 0.077 | 0.152 | 0.943 | 5.966 | 0.000 |
| consc_t1 ~ goals | -0.388 | -0.471 | -0.305 | -0.644 | -9.162 | 0.000 |
| d_consc_1 ~ consc_t1 | -0.149 | -0.256 | -0.042 | -0.286 | -2.740 | 0.006 |
| d_consc_1 ~ goals | -0.029 | -0.100 | 0.042 | -0.092 | -0.796 | 0.426 |
| goals ~~ goals | 1.232 | 0.954 | 1.510 | 1.000 | 8.696 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_consc_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5479.454 | 5578.699 | 0.998 | 0.018 | 0.03 |
# parameters of interest
params_lcs_consc_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_consc_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("consc_t2 ~ consc_t1", "d_consc_1 =~ consc_t2", "d_consc_1 ~ consc_t1", # change parameters
"d_consc_1 ~ frequ", "frequ ~~ frequ", "consc_t1 ~ frequ", # frequency of self improvement
"d_consc_1 ~1 ", "consc_t1 ~1 ", "", # means
"d_consc_1 ~~ d_consc_1"))
kable(params_lcs_consc_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| consc_t2 ~ consc_t1 | 1.000 | 1.000 | 1.000 | 0.984 | NA | NA |
| d_consc_1 =~ consc_t2 | 1.000 | 1.000 | 1.000 | 0.512 | NA | NA |
| d_consc_1 ~1 | 0.499 | 0.256 | 0.743 | 1.434 | 4.019 | 0.000 |
| consc_t1 ~1 | 3.380 | 3.299 | 3.461 | 5.054 | 81.596 | 0.000 |
| d_consc_1 ~~ d_consc_1 | 0.114 | 0.076 | 0.152 | 0.940 | 5.835 | 0.000 |
| consc_t1 ~ frequ | 0.128 | -0.016 | 0.272 | 0.141 | 1.743 | 0.081 |
| d_consc_1 ~ consc_t1 | -0.126 | -0.195 | -0.057 | -0.241 | -3.568 | 0.000 |
| d_consc_1 ~ frequ | 0.043 | -0.031 | 0.118 | 0.091 | 1.136 | 0.256 |
| frequ ~~ frequ | 0.539 | 0.359 | 0.719 | 1.000 | 5.879 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6521.254 | 6620.499 | 0.992 | 0.042 | 0.029 |
# parameters of interest
params_lcs_neuro_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_neuro_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ goals", "goals ~~ goals", "neuro_t1 ~ goals", # acceptance goals
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.462 | NA | NA |
| d_neuro_1 ~1 | 0.244 | -0.016 | 0.504 | 0.655 | 1.838 | 0.066 |
| neuro_t1 ~1 | 3.250 | 3.157 | 3.342 | 4.077 | 68.940 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.131 | 0.089 | 0.173 | 0.943 | 6.085 | 0.000 |
| neuro_t1 ~ goals | 0.410 | 0.301 | 0.519 | 0.517 | 7.384 | 0.000 |
| d_neuro_1 ~ neuro_t1 | -0.129 | -0.207 | -0.051 | -0.277 | -3.243 | 0.001 |
| d_neuro_1 ~ goals | 0.046 | -0.025 | 0.117 | 0.125 | 1.276 | 0.202 |
| goals ~~ goals | 1.009 | 0.733 | 1.285 | 1.000 | 7.172 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_neuro_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5686.154 | 5785.398 | 1 | 0 | 0.04 |
# parameters of interest
params_lcs_neuro_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_neuro_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("neuro_t2 ~ neuro_t1", "d_neuro_1 =~ neuro_t2", "d_neuro_1 ~ neuro_t1", # change parameters
"d_neuro_1 ~ frequ", "frequ ~~ frequ", "neuro_t1 ~ frequ", # frequency of self improvement
"d_neuro_1 ~1 ", "neuro_t1 ~1 ", "", # means
"d_neuro_1 ~~ d_neuro_1"))
kable(params_lcs_neuro_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| neuro_t2 ~ neuro_t1 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_neuro_1 =~ neuro_t2 | 1.000 | 1.000 | 1.000 | 0.463 | NA | NA |
| d_neuro_1 ~1 | 0.117 | -0.089 | 0.322 | 0.312 | 1.110 | 0.267 |
| neuro_t1 ~1 | 3.250 | 3.158 | 3.342 | 4.066 | 68.947 | 0.000 |
| d_neuro_1 ~~ d_neuro_1 | 0.128 | 0.085 | 0.170 | 0.914 | 5.846 | 0.000 |
| neuro_t1 ~ frequ | 0.107 | -0.036 | 0.251 | 0.100 | 1.466 | 0.143 |
| d_neuro_1 ~ neuro_t1 | -0.090 | -0.153 | -0.027 | -0.193 | -2.819 | 0.005 |
| d_neuro_1 ~ frequ | -0.101 | -0.192 | -0.011 | -0.201 | -2.188 | 0.029 |
| frequ ~~ frequ | 0.554 | 0.373 | 0.735 | 1.000 | 5.993 | 0.000 |
Results summary (goals = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 6027.912 | 6127.157 | 0.993 | 0.037 | 0.032 |
# parameters of interest
params_lcs_openn_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_openn_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ goals", "goals ~~ goals", "openn_t1 ~ goals", # acceptance goals
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.021 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.408 | NA | NA |
| d_openn_1 ~1 | 0.435 | 0.102 | 0.767 | 1.838 | 2.564 | 0.010 |
| openn_t1 ~1 | 3.565 | 3.490 | 3.640 | 6.028 | 93.319 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.052 | 0.025 | 0.080 | 0.937 | 3.711 | 0.000 |
| openn_t1 ~ goals | -0.396 | -0.502 | -0.290 | -0.524 | -7.339 | 0.000 |
| d_openn_1 ~ openn_t1 | -0.101 | -0.193 | -0.009 | -0.252 | -2.143 | 0.032 |
| d_openn_1 ~ goals | -0.001 | -0.079 | 0.078 | -0.002 | -0.017 | 0.987 |
| goals ~~ goals | 0.611 | 0.396 | 0.826 | 1.000 | 5.579 | 0.000 |
Results summary (frequ = frequency of self improvement behavior):
# model fit
kable(broom::glance(fit_mi_lcs_openn_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 26 | 5378.535 | 5477.78 | 0.995 | 0.029 | 0.035 |
# parameters of interest
params_lcs_openn_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_openn_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("openn_t2 ~ openn_t1", "d_openn_1 =~ openn_t2", "d_openn_1 ~ openn_t1", # change parameters
"d_openn_1 ~ frequ", "frequ ~~ frequ", "openn_t1 ~ frequ", # frequency of self improvement
"d_openn_1 ~1 ", "openn_t1 ~1 ", "", # means
"d_openn_1 ~~ d_openn_1"))
kable(params_lcs_openn_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| openn_t2 ~ openn_t1 | 1.000 | 1.000 | 1.000 | 1.021 | NA | NA |
| d_openn_1 =~ openn_t2 | 1.000 | 1.000 | 1.000 | 0.407 | NA | NA |
| d_openn_1 ~1 | 0.484 | 0.220 | 0.749 | 2.028 | 3.586 | 0.000 |
| openn_t1 ~1 | 3.564 | 3.490 | 3.639 | 5.955 | 93.281 | 0.000 |
| d_openn_1 ~~ d_openn_1 | 0.051 | 0.024 | 0.077 | 0.892 | 3.780 | 0.000 |
| openn_t1 ~ frequ | 0.142 | 0.034 | 0.249 | 0.173 | 2.576 | 0.010 |
| d_openn_1 ~ openn_t1 | -0.115 | -0.186 | -0.043 | -0.287 | -3.141 | 0.002 |
| d_openn_1 ~ frequ | 0.071 | -0.010 | 0.153 | 0.218 | 1.715 | 0.086 |
| frequ ~~ frequ | 0.533 | 0.354 | 0.711 | 1.000 | 5.851 | 0.000 |
Run models for all facets with a template & loop:
# create templates:
# 1st, for facet-specific acceptance goal
facet_template_mod_goal_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets -> changes in ideal self
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
mod_name = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), facet_model_fit))
}
# loop across 15 facets -> changes in current self
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[5]], item_nrs) # current instead of ideal!
mod_name = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp7")), template_filled)) # current instead of ideal!
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_specif_hyp7")), facet_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for ideal self ratings)
facet_template_mod_goal_accept_fu <- '
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2
facet_t3 =~ 1*ind1_t3 + lamb2*ind2_t3 + lamb3*ind3_t3 + lamb4*ind4_t3 # This specifies the measurement model for facet at T3 (with equality constraints)
facet_t3 ~ 1*facet_t2 # This parameter regresses facet_t3 perfectly on facet_t2
d_facet_1 =~ 1*facet_t3 # This defines the latent change score factor as measured perfectly by scores on facet_t3
facet_t3 ~ 0*1 # This line constrains the intercept of facet_t3 to 0
facet_t3 ~~ 0*facet_t3 # This fixes the variance of facet_t3 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t2 ~ 1 # This estimates the intercept of facet_t2
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t2 ~~ facet_t2 # This estimates the variance of facet_t2
facet_t2 ~ ind_goal # This estimates the moderation effect on personality at T2
d_facet_1 ~ facet_t2 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change score
ind1_t2 ~~ ind1_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind2_t2 ~~ ind2_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind3_t2 ~~ ind3_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind4_t2 ~~ ind4_t3 # This allows residual covariance on indicator X4 across T2 and T3
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t3 ~~ res1*ind1_t3 # This allows residual variance on indicator X1 at T3
ind2_t3 ~~ res2*ind2_t3 # This allows residual variance on indicator X2 at T3
ind3_t3 ~~ res3*ind3_t3 # This allows residual variance on indicator X3 at T3
ind4_t3 ~~ res4*ind4_t3 # This allows residual variance on indicator X4 at T3
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind2_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind3_t3 ~ m3*1 # This estimates the intercept of X3 at T3
ind4_t3 ~ m4*1 # This estimates the intercept of X4 at T3
ind_goal ~~ ind_goal
ind_goal ~ 1
'
# loop across 15 facets -> changes in ideal self (from T2 to T3)
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
mod_name = paste0("sa07_", str_pad(i-5, 2, pad = "0"), "_t1")
template_filled <- str_replace_all(facet_template_mod_goal_accept_fu,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4],
"ind_goal" = mod_name))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_specif_hyp7_fu")), facet_model_fit))
}
# 2nd, for frequency of self-acceptance behavior
facet_template_mod_frequ_accept <- '
facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1
d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2
facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0
facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t1 ~ 1 # This estimates the intercept of facet_t1
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1
facet_t1 ~ frequ # This estimates the moderation effect on personality at T1
d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2
ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2
ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2
ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2
ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1
ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1
ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1
ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1
ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1
ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 15 facets -> changes in ideal self
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), facet_model_fit))
}
# loop across 15 facets -> changes in current self
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[5]], item_nrs) # current instead of ideal!
template_filled <- str_replace_all(facet_template_mod_frequ_accept,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp7")), template_filled)) # current instead of ideal!
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[5], 6), "_frequ_hyp7")), facet_model_fit))
}
# we also need separate models for follow-up (fu), that is, changes from T2 to T3 (only for ideal self ratings)
facet_template_mod_frequ_accept_fu <- '
facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2
facet_t3 =~ 1*ind1_t3 + lamb2*ind2_t3 + lamb3*ind3_t3 + lamb4*ind4_t3 # This specifies the measurement model for facet at T3 (with equality constraints)
frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderator
facet_t3 ~ 1*facet_t2 # This parameter regresses facet_t3 perfectly on facet_t2
d_facet_1 =~ 1*facet_t3 # This defines the latent change score factor as measured perfectly by scores on facet_t3
facet_t3 ~ 0*1 # This line constrains the intercept of facet_t3 to 0
facet_t3 ~~ 0*facet_t3 # This fixes the variance of facet_t3 to 0
d_facet_1 ~ 1 # This estimates the intercept of the change score
facet_t2 ~ 1 # This estimates the intercept of facet_t2
d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores
facet_t2 ~~ facet_t2 # This estimates the variance of facet_t2
facet_t2 ~ frequ # This estimates the moderation effect on personality at T2
d_facet_1 ~ facet_t2 + frequ # This estimates the self-feedback parameter and the moderation effect on the change score
frequ ~ 0*1 # This fixes the intercept of the moderator to 0
frequ ~~ frequ # This estimates the variance of the moderator
ind1_t2 ~~ ind1_t3 # This allows residual covariance on indicator X1 across T2 and T3
ind2_t2 ~~ ind2_t3 # This allows residual covariance on indicator X2 across T2 and T3
ind3_t2 ~~ ind3_t3 # This allows residual covariance on indicator X3 across T2 and T3
ind4_t2 ~~ ind4_t3 # This allows residual covariance on indicator X4 across T2 and T3
ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2
ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2
ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2
ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2
ind1_t3 ~~ res1*ind1_t3 # This allows residual variance on indicator X1 at T3
ind2_t3 ~~ res2*ind2_t3 # This allows residual variance on indicator X2 at T3
ind3_t3 ~~ res3*ind3_t3 # This allows residual variance on indicator X3 at T3
ind4_t3 ~~ res4*ind4_t3 # This allows residual variance on indicator X4 at T3
ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2
ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2
ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2
ind1_t3 ~ 0*1 # This constrains the intercept of X1 to 0 at T3
ind2_t3 ~ m2*1 # This estimates the intercept of X2 at T3
ind3_t3 ~ m3*1 # This estimates the intercept of X3 at T3
ind4_t3 ~ m4*1 # This estimates the intercept of X4 at T3
sa04_01_t2 ~~ sa04_01_t2
sa04_02_t2 ~~ sa04_02_t2
sa04_03_t2 ~~ sa04_03_t2
sa04_01_t2 ~ 1
sa04_02_t2 ~ 1
sa04_03_t2 ~ 1
'
# loop across 15 facets -> changes in ideal self
for (i in 6:length(b5_vars)) {
item_nrs = b5_vars[[i]][[1]]
short_name = str_trunc(names(b5_vars)[i], 5, ellipsis = "")
# use BFI version combined pre&post ideal
items = paste0(bfi_versions[[6]], item_nrs)
template_filled <- str_replace_all(facet_template_mod_frequ_accept_fu,
c("facet" = short_name,
"ind1" = items[1], "ind2" = items[2], "ind3" = items[3], "ind4" = items[4]))
facet_model_fit <- lavaan(template_filled, data=df_sbsa2_wide_pers_sa_mod_fu, estimator='mlr', fixed.x=FALSE, missing='fiml')
eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7_fu")), template_filled))
eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_",
str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7_fu")), facet_model_fit))
} Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8062.214 | 8150.007 | 0.933 | 0.048 | 0.055 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", "d_socia_1 ~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.894 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.483 | NA | NA |
| d_socia_1 ~1 | 0.060 | -1.017 | 1.137 | 0.314 | 0.109 | 0.913 |
| socia_t1 ~1 | 4.078 | 3.886 | 4.271 | 11.548 | 41.478 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.032 | -0.025 | 0.089 | 0.883 | 1.108 | 0.268 |
| d_socia_1 ~ socia_t1 | 0.010 | -0.251 | 0.271 | 0.019 | 0.076 | 0.939 |
| d_socia_1 ~ sa07_01_t1 | -0.049 | -0.096 | -0.001 | -0.344 | -2.013 | 0.044 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.826 | 1.634 | 2.019 | 1.000 | 18.622 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9292.668 | 9407.182 | 0.938 | 0.049 | 0.057 |
# parameters of interest
params_lcs_socia_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_socia_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 0.895 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_socia_1 ~1 | 0.001 | -1.133 | 1.135 | 0.003 | 0.001 | 0.999 |
| socia_t1 ~1 | 4.226 | 4.145 | 4.307 | 11.987 | 102.547 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.037 | -0.021 | 0.094 | 0.992 | 1.251 | 0.211 |
| socia_t1 ~ frequ | 0.112 | 0.005 | 0.219 | 0.235 | 2.052 | 0.040 |
| d_socia_1 ~ socia_t1 | -0.015 | -0.279 | 0.249 | -0.027 | -0.109 | 0.913 |
| d_socia_1 ~ frequ | -0.021 | -0.118 | 0.077 | -0.080 | -0.415 | 0.678 |
| frequ ~~ frequ | 0.549 | 0.370 | 0.728 | 1.000 | 6.007 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7980.554 | 8068.348 | 1 | 0 | 0.04 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", "d_asser_1 ~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.922 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.420 | NA | NA |
| d_asser_1 ~1 | 0.004 | -0.946 | 0.954 | 0.023 | 0.008 | 0.994 |
| asser_t1 ~1 | 4.025 | 3.846 | 4.204 | 11.191 | 44.069 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.026 | -0.013 | 0.065 | 0.961 | 1.296 | 0.195 |
| d_asser_1 ~ asser_t1 | -0.026 | -0.252 | 0.199 | -0.058 | -0.228 | 0.820 |
| d_asser_1 ~ sa07_02_t1 | 0.024 | -0.013 | 0.062 | 0.196 | 1.262 | 0.207 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.747 | 1.573 | 1.921 | 1.000 | 19.717 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9217.907 | 9332.42 | 1 | 0 | 0.041 |
# parameters of interest
params_lcs_asser_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_asser_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.922 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.419 | NA | NA |
| d_asser_1 ~1 | 0.015 | -1.001 | 1.030 | 0.089 | 0.029 | 0.977 |
| asser_t1 ~1 | 4.134 | 4.044 | 4.225 | 11.256 | 89.400 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.028 | -0.014 | 0.069 | 0.997 | 1.311 | 0.190 |
| asser_t1 ~ frequ | 0.147 | 0.045 | 0.250 | 0.297 | 2.812 | 0.005 |
| d_asser_1 ~ asser_t1 | -0.010 | -0.251 | 0.232 | -0.022 | -0.080 | 0.936 |
| d_asser_1 ~ frequ | -0.009 | -0.094 | 0.077 | -0.039 | -0.203 | 0.839 |
| frequ ~~ frequ | 0.550 | 0.374 | 0.726 | 1.000 | 6.127 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7715.501 | 7803.294 | 0.92 | 0.049 | 0.056 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", "d_energ_1 ~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.001 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.769 | NA | NA |
| d_energ_1 ~1 | 0.668 | 0.266 | 1.071 | 4.147 | 3.253 | 0.001 |
| energ_t1 ~1 | 2.257 | 2.120 | 2.393 | 10.747 | 32.461 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.022 | -0.016 | 0.060 | 0.851 | 1.131 | 0.258 |
| d_energ_1 ~ energ_t1 | -0.297 | -0.481 | -0.113 | -0.387 | -3.167 | 0.002 |
| d_energ_1 ~ sa07_03_t1 | -0.002 | -0.021 | 0.017 | -0.016 | -0.203 | 0.839 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.851 | 1.650 | 2.052 | 1.000 | 18.028 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 8918.395 | 9032.908 | 0.943 | 0.046 | 0.056 |
# parameters of interest
params_lcs_energ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_energ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.014 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.763 | NA | NA |
| d_energ_1 ~1 | 0.797 | 0.355 | 1.238 | 4.548 | 3.537 | 0.000 |
| energ_t1 ~1 | 2.222 | 2.106 | 2.339 | 9.538 | 37.289 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.024 | -0.007 | 0.055 | 0.788 | 1.518 | 0.129 |
| energ_t1 ~ frequ | -0.105 | -0.188 | -0.021 | -0.327 | -2.457 | 0.014 |
| d_energ_1 ~ energ_t1 | -0.359 | -0.559 | -0.159 | -0.477 | -3.518 | 0.000 |
| d_energ_1 ~ frequ | -0.060 | -0.125 | 0.004 | -0.252 | -1.836 | 0.066 |
| frequ ~~ frequ | 0.532 | 0.354 | 0.710 | 1.000 | 5.855 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8341.376 | 8429.169 | 0.96 | 0.041 | 0.047 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", "d_compa_1 ~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.601 | NA | NA |
| d_compa_1 ~1 | 0.876 | -0.087 | 1.838 | 2.470 | 1.783 | 0.075 |
| compa_t1 ~1 | 4.489 | 4.329 | 4.648 | 7.476 | 55.172 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.112 | 0.019 | 0.206 | 0.894 | 2.362 | 0.018 |
| d_compa_1 ~ compa_t1 | -0.195 | -0.401 | 0.011 | -0.331 | -1.858 | 0.063 |
| d_compa_1 ~ sa07_04_t1 | -0.009 | -0.068 | 0.051 | -0.035 | -0.287 | 0.774 |
| sa07_04_t1 ~~ sa07_04_t1 | 2.024 | 1.813 | 2.235 | 1.000 | 18.799 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9539.194 | 9653.708 | 0.975 | 0.033 | 0.043 |
# parameters of interest
params_lcs_compa_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_compa_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.604 | NA | NA |
| d_compa_1 ~1 | 0.869 | -0.035 | 1.773 | 2.351 | 1.885 | 0.059 |
| compa_t1 ~1 | 4.291 | 4.196 | 4.385 | 6.900 | 88.970 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.122 | 0.022 | 0.222 | 0.892 | 2.386 | 0.017 |
| compa_t1 ~ frequ | 0.142 | -0.015 | 0.299 | 0.168 | 1.773 | 0.076 |
| d_compa_1 ~ compa_t1 | -0.198 | -0.402 | 0.005 | -0.333 | -1.908 | 0.056 |
| d_compa_1 ~ frequ | 0.028 | -0.098 | 0.153 | 0.055 | 0.433 | 0.665 |
| frequ ~~ frequ | 0.544 | 0.365 | 0.724 | 1.000 | 5.935 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 6475.254 | 6563.048 | 0.911 | 0.077 | 0.061 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", "d_respe_1 ~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.999 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.391 | NA | NA |
| d_respe_1 ~1 | 0.380 | -0.449 | 1.209 | 2.533 | 0.899 | 0.369 |
| respe_t1 ~1 | 4.709 | 4.615 | 4.803 | 12.287 | 98.463 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.022 | -0.004 | 0.047 | 0.959 | 1.658 | 0.097 |
| d_respe_1 ~ respe_t1 | -0.079 | -0.250 | 0.092 | -0.202 | -0.909 | 0.363 |
| d_respe_1 ~ sa07_05_t1 | -0.007 | -0.043 | 0.028 | -0.066 | -0.412 | 0.680 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.744 | 1.473 | 2.015 | 1.000 | 12.613 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 7710.582 | 7825.096 | 0.967 | 0.044 | 0.045 |
# parameters of interest
params_lcs_respe_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respe_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.389 | NA | NA |
| d_respe_1 ~1 | 0.429 | -0.404 | 1.263 | 2.916 | 1.010 | 0.313 |
| respe_t1 ~1 | 4.625 | 4.560 | 4.690 | 12.266 | 139.842 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.020 | -0.004 | 0.044 | 0.922 | 1.663 | 0.096 |
| respe_t1 ~ frequ | 0.121 | 0.038 | 0.203 | 0.236 | 2.862 | 0.004 |
| d_respe_1 ~ respe_t1 | -0.093 | -0.269 | 0.083 | -0.238 | -1.039 | 0.299 |
| d_respe_1 ~ frequ | 0.043 | -0.026 | 0.111 | 0.213 | 1.223 | 0.221 |
| frequ ~~ frequ | 0.545 | 0.367 | 0.723 | 1.000 | 5.998 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8054.899 | 8142.692 | 0.965 | 0.039 | 0.043 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", "d_trust_1 ~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.106 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.594 | NA | NA |
| d_trust_1 ~1 | 0.475 | 0.175 | 0.774 | 1.731 | 3.103 | 0.002 |
| trust_t1 ~1 | 1.967 | 1.795 | 2.140 | 3.858 | 22.330 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.060 | 0.002 | 0.119 | 0.805 | 2.015 | 0.044 |
| d_trust_1 ~ trust_t1 | -0.236 | -0.383 | -0.090 | -0.440 | -3.168 | 0.002 |
| d_trust_1 ~ sa07_06_t1 | -0.011 | -0.053 | 0.031 | -0.057 | -0.531 | 0.595 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.909 | 1.711 | 2.108 | 1.000 | 18.829 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9255.534 | 9370.047 | 0.967 | 0.039 | 0.048 |
# parameters of interest
params_lcs_trust_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_trust_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.107 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.604 | NA | NA |
| d_trust_1 ~1 | 0.508 | 0.211 | 0.805 | 1.857 | 3.352 | 0.001 |
| trust_t1 ~1 | 1.946 | 1.847 | 2.045 | 3.878 | 38.594 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.055 | -0.002 | 0.113 | 0.739 | 1.896 | 0.058 |
| trust_t1 ~ frequ | -0.134 | -0.263 | -0.005 | -0.197 | -2.032 | 0.042 |
| d_trust_1 ~ trust_t1 | -0.269 | -0.424 | -0.113 | -0.493 | -3.390 | 0.001 |
| d_trust_1 ~ frequ | -0.097 | -0.191 | -0.003 | -0.262 | -2.030 | 0.042 |
| frequ ~~ frequ | 0.546 | 0.366 | 0.725 | 1.000 | 5.954 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7010.137 | 7097.93 | 0.978 | 0.029 | 0.045 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", "d_organ_1 ~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.972 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.604 | NA | NA |
| d_organ_1 ~1 | 0.219 | -0.141 | 0.579 | 0.829 | 1.191 | 0.234 |
| organ_t1 ~1 | 1.724 | 1.558 | 1.890 | 4.061 | 20.374 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.064 | 0.013 | 0.115 | 0.916 | 2.460 | 0.014 |
| d_organ_1 ~ organ_t1 | -0.166 | -0.395 | 0.063 | -0.267 | -1.420 | 0.156 |
| d_organ_1 ~ sa07_07_t1 | 0.022 | -0.018 | 0.062 | 0.120 | 1.085 | 0.278 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.070 | 1.888 | 2.251 | 1.000 | 22.344 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 8190.663 | 8305.176 | 0.992 | 0.017 | 0.043 |
# parameters of interest
params_lcs_organ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_organ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 0.970 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.605 | NA | NA |
| d_organ_1 ~1 | 0.296 | -0.111 | 0.702 | 1.130 | 1.426 | 0.154 |
| organ_t1 ~1 | 1.754 | 1.651 | 1.857 | 4.180 | 33.482 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.063 | 0.012 | 0.115 | 0.927 | 2.423 | 0.015 |
| organ_t1 ~ frequ | -0.116 | -0.231 | -0.001 | -0.205 | -1.986 | 0.047 |
| d_organ_1 ~ organ_t1 | -0.171 | -0.415 | 0.072 | -0.275 | -1.381 | 0.167 |
| d_organ_1 ~ frequ | -0.023 | -0.120 | 0.074 | -0.065 | -0.466 | 0.641 |
| frequ ~~ frequ | 0.545 | 0.366 | 0.723 | 1.000 | 5.982 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 6374.471 | 6462.264 | 0.909 | 0.069 | 0.066 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", "d_produ_1 ~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.046 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.676 | NA | NA |
| d_produ_1 ~1 | 0.285 | 0.024 | 0.545 | 1.193 | 2.141 | 0.032 |
| produ_t1 ~1 | 1.576 | 1.436 | 1.715 | 4.265 | 22.137 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.047 | 0.009 | 0.085 | 0.825 | 2.405 | 0.016 |
| d_produ_1 ~ produ_t1 | -0.253 | -0.425 | -0.080 | -0.391 | -2.867 | 0.004 |
| d_produ_1 ~ sa07_08_t1 | 0.026 | -0.004 | 0.056 | 0.150 | 1.688 | 0.091 |
| sa07_08_t1 ~~ sa07_08_t1 | 1.880 | 1.691 | 2.069 | 1.000 | 19.477 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 7584.382 | 7698.896 | 0.922 | 0.064 | 0.064 |
# parameters of interest
params_lcs_produ_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_produ_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.675 | NA | NA |
| d_produ_1 ~1 | 0.397 | 0.143 | 0.651 | 1.656 | 3.068 | 0.002 |
| produ_t1 ~1 | 1.585 | 1.507 | 1.663 | 4.281 | 39.888 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.045 | 0.008 | 0.082 | 0.782 | 2.367 | 0.018 |
| produ_t1 ~ frequ | -0.060 | -0.144 | 0.025 | -0.117 | -1.377 | 0.168 |
| d_produ_1 ~ produ_t1 | -0.269 | -0.440 | -0.098 | -0.416 | -3.088 | 0.002 |
| d_produ_1 ~ frequ | -0.088 | -0.156 | -0.020 | -0.267 | -2.540 | 0.011 |
| frequ ~~ frequ | 0.532 | 0.355 | 0.710 | 1.000 | 5.875 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7227.03 | 7314.824 | 0.959 | 0.047 | 0.051 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", "d_respo_1 ~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.912 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.623 | NA | NA |
| d_respo_1 ~1 | 0.669 | -0.246 | 1.584 | 2.523 | 1.433 | 0.152 |
| respo_t1 ~1 | 4.294 | 4.156 | 4.433 | 11.063 | 60.742 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.066 | 0.015 | 0.118 | 0.946 | 2.524 | 0.012 |
| d_respo_1 ~ respo_t1 | -0.140 | -0.349 | 0.070 | -0.204 | -1.306 | 0.191 |
| d_respo_1 ~ sa07_09_t1 | -0.024 | -0.058 | 0.011 | -0.129 | -1.346 | 0.178 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.069 | 1.860 | 2.278 | 1.000 | 19.415 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 8408.147 | 8522.66 | 0.972 | 0.038 | 0.049 |
# parameters of interest
params_lcs_respo_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respo_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 0.912 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.626 | NA | NA |
| d_respo_1 ~1 | 0.685 | -0.220 | 1.590 | 2.524 | 1.483 | 0.138 |
| respo_t1 ~1 | 4.239 | 4.135 | 4.343 | 10.726 | 80.005 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.067 | 0.016 | 0.119 | 0.916 | 2.566 | 0.010 |
| respo_t1 ~ frequ | 0.084 | -0.021 | 0.189 | 0.156 | 1.568 | 0.117 |
| d_respo_1 ~ respo_t1 | -0.157 | -0.365 | 0.051 | -0.229 | -1.483 | 0.138 |
| d_respo_1 ~ frequ | 0.080 | 0.002 | 0.159 | 0.217 | 1.999 | 0.046 |
| frequ ~~ frequ | 0.541 | 0.361 | 0.720 | 1.000 | 5.904 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7999.913 | 8087.706 | 0.922 | 0.05 | 0.059 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", "d_anxie_1 ~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.180 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.730 | NA | NA |
| d_anxie_1 ~1 | 1.543 | 0.480 | 2.607 | 6.100 | 2.844 | 0.004 |
| anxie_t1 ~1 | 4.554 | 4.405 | 4.703 | 11.143 | 59.850 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.045 | 0.005 | 0.086 | 0.711 | 2.210 | 0.027 |
| d_anxie_1 ~ anxie_t1 | -0.333 | -0.557 | -0.109 | -0.538 | -2.912 | 0.004 |
| d_anxie_1 ~ sa07_10_t1 | -0.005 | -0.037 | 0.027 | -0.028 | -0.302 | 0.762 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.115 | 1.921 | 2.308 | 1.000 | 21.431 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9172.921 | 9287.434 | 0.969 | 0.034 | 0.05 |
# parameters of interest
params_lcs_anxie_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_anxie_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 1.183 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.726 | NA | NA |
| d_anxie_1 ~1 | 1.460 | 0.384 | 2.536 | 5.662 | 2.659 | 0.008 |
| anxie_t1 ~1 | 4.530 | 4.450 | 4.609 | 10.777 | 111.419 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.047 | 0.004 | 0.089 | 0.700 | 2.152 | 0.031 |
| anxie_t1 ~ frequ | 0.120 | 0.010 | 0.231 | 0.213 | 2.144 | 0.032 |
| d_anxie_1 ~ anxie_t1 | -0.318 | -0.550 | -0.085 | -0.518 | -2.680 | 0.007 |
| d_anxie_1 ~ frequ | -0.034 | -0.118 | 0.049 | -0.099 | -0.809 | 0.418 |
| frequ ~~ frequ | 0.554 | 0.373 | 0.734 | 1.000 | 6.014 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 6424.347 | 6512.141 | 0.94 | 0.055 | 0.052 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", "d_depre_1 ~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.157 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.700 | NA | NA |
| d_depre_1 ~1 | 1.393 | 0.638 | 2.147 | 5.262 | 3.616 | 0.000 |
| depre_t1 ~1 | 4.440 | 4.302 | 4.578 | 10.146 | 63.122 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.052 | 0.011 | 0.093 | 0.738 | 2.468 | 0.014 |
| d_depre_1 ~ depre_t1 | -0.309 | -0.471 | -0.147 | -0.511 | -3.741 | 0.000 |
| d_depre_1 ~ sa07_11_t1 | -0.004 | -0.033 | 0.026 | -0.022 | -0.264 | 0.792 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.106 | 1.918 | 2.294 | 1.000 | 21.989 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 7599.7 | 7714.213 | 0.944 | 0.053 | 0.057 |
# parameters of interest
params_lcs_depre_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_depre_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 1.156 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.700 | NA | NA |
| d_depre_1 ~1 | 1.382 | 0.644 | 2.121 | 5.108 | 3.668 | 0.000 |
| depre_t1 ~1 | 4.443 | 4.368 | 4.519 | 9.939 | 115.332 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.054 | 0.011 | 0.097 | 0.740 | 2.451 | 0.014 |
| depre_t1 ~ frequ | 0.109 | -0.004 | 0.222 | 0.180 | 1.895 | 0.058 |
| d_depre_1 ~ depre_t1 | -0.310 | -0.472 | -0.148 | -0.512 | -3.750 | 0.000 |
| d_depre_1 ~ frequ | 0.003 | -0.067 | 0.073 | 0.008 | 0.082 | 0.935 |
| frequ ~~ frequ | 0.543 | 0.363 | 0.723 | 1.000 | 5.924 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7401.116 | 7488.909 | 0.954 | 0.042 | 0.051 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", "d_volat_1 ~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.020 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.570 | NA | NA |
| d_volat_1 ~1 | 0.330 | -0.017 | 0.677 | 1.298 | 1.867 | 0.062 |
| volat_t1 ~1 | 1.425 | 1.288 | 1.561 | 3.128 | 20.433 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.057 | 0.002 | 0.112 | 0.879 | 2.015 | 0.044 |
| d_volat_1 ~ volat_t1 | -0.156 | -0.380 | 0.068 | -0.279 | -1.367 | 0.172 |
| d_volat_1 ~ sa07_12_t1 | -0.030 | -0.070 | 0.010 | -0.152 | -1.455 | 0.146 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.709 | 1.531 | 1.887 | 1.000 | 18.847 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 8655.333 | 8769.846 | 0.96 | 0.041 | 0.051 |
# parameters of interest
params_lcs_volat_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_volat_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_volat_1 ~1 | 0.293 | -0.061 | 0.646 | 1.140 | 1.623 | 0.105 |
| volat_t1 ~1 | 1.646 | 1.566 | 1.726 | 3.576 | 40.293 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.058 | 0.002 | 0.115 | 0.887 | 2.041 | 0.041 |
| volat_t1 ~ frequ | -0.110 | -0.234 | 0.013 | -0.176 | -1.747 | 0.081 |
| d_volat_1 ~ volat_t1 | -0.183 | -0.412 | 0.046 | -0.328 | -1.565 | 0.118 |
| d_volat_1 ~ frequ | -0.052 | -0.153 | 0.050 | -0.148 | -0.999 | 0.318 |
| frequ ~~ frequ | 0.543 | 0.365 | 0.722 | 1.000 | 5.956 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7804.168 | 7891.962 | 0.972 | 0.035 | 0.05 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", "d_curio_1 ~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.547 | NA | NA |
| d_curio_1 ~1 | 0.885 | 0.088 | 1.683 | 4.194 | 2.177 | 0.029 |
| curio_t1 ~1 | 4.653 | 4.529 | 4.777 | 11.116 | 73.271 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.037 | -0.004 | 0.079 | 0.838 | 1.756 | 0.079 |
| d_curio_1 ~ curio_t1 | -0.198 | -0.364 | -0.031 | -0.392 | -2.328 | 0.020 |
| d_curio_1 ~ sa07_13_t1 | 0.006 | -0.030 | 0.042 | 0.035 | 0.309 | 0.757 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.724 | 1.530 | 1.918 | 1.000 | 17.438 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9065.942 | 9180.456 | 0.983 | 0.027 | 0.049 |
# parameters of interest
params_lcs_curio_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_curio_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.084 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.543 | NA | NA |
| d_curio_1 ~1 | 0.914 | 0.203 | 1.626 | 4.446 | 2.518 | 0.012 |
| curio_t1 ~1 | 4.458 | 4.383 | 4.533 | 10.853 | 116.407 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.035 | -0.005 | 0.075 | 0.830 | 1.725 | 0.084 |
| curio_t1 ~ frequ | 0.019 | -0.083 | 0.121 | 0.035 | 0.369 | 0.712 |
| d_curio_1 ~ curio_t1 | -0.201 | -0.358 | -0.045 | -0.402 | -2.527 | 0.012 |
| d_curio_1 ~ frequ | 0.029 | -0.040 | 0.098 | 0.104 | 0.817 | 0.414 |
| frequ ~~ frequ | 0.549 | 0.370 | 0.728 | 1.000 | 6.009 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7878.706 | 7966.499 | 0.97 | 0.044 | 0.044 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", "d_aesth_1 ~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.972 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.609 | NA | NA |
| d_aesth_1 ~1 | 0.468 | 0.008 | 0.928 | 2.754 | 1.993 | 0.046 |
| aesth_t1 ~1 | 2.767 | 2.576 | 2.958 | 10.206 | 28.434 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.027 | 0.000 | 0.054 | 0.928 | 1.926 | 0.054 |
| d_aesth_1 ~ aesth_t1 | -0.172 | -0.339 | -0.005 | -0.274 | -2.020 | 0.043 |
| d_aesth_1 ~ sa07_14_t1 | 0.004 | -0.015 | 0.023 | 0.033 | 0.413 | 0.679 |
| sa07_14_t1 ~~ sa07_14_t1 | 2.002 | 1.823 | 2.180 | 1.000 | 21.957 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9077.014 | 9191.528 | 0.979 | 0.035 | 0.047 |
# parameters of interest
params_lcs_aesth_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_aesth_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.969 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.609 | NA | NA |
| d_aesth_1 ~1 | 0.542 | 0.072 | 1.011 | 3.330 | 2.261 | 0.024 |
| aesth_t1 ~1 | 2.903 | 2.767 | 3.038 | 11.217 | 42.031 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.024 | -0.001 | 0.049 | 0.904 | 1.845 | 0.065 |
| aesth_t1 ~ frequ | -0.091 | -0.159 | -0.024 | -0.260 | -2.663 | 0.008 |
| d_aesth_1 ~ aesth_t1 | -0.193 | -0.358 | -0.028 | -0.307 | -2.296 | 0.022 |
| d_aesth_1 ~ frequ | -0.038 | -0.095 | 0.019 | -0.170 | -1.300 | 0.194 |
| frequ ~~ frequ | 0.540 | 0.362 | 0.718 | 1.000 | 5.940 | 0.000 |
Results summary (*sa07_$$_t1* = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7447.667 | 7535.461 | 0.908 | 0.07 | 0.06 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp7 <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", "d_imagi_1 ~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.047 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.654 | NA | NA |
| d_imagi_1 ~1 | 1.207 | 0.315 | 2.100 | 3.643 | 2.651 | 0.008 |
| imagi_t1 ~1 | 4.711 | 4.573 | 4.848 | 8.880 | 67.101 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.092 | 0.030 | 0.154 | 0.840 | 2.923 | 0.003 |
| d_imagi_1 ~ imagi_t1 | -0.251 | -0.437 | -0.065 | -0.402 | -2.642 | 0.008 |
| d_imagi_1 ~ sa07_15_t1 | -0.027 | -0.067 | 0.013 | -0.119 | -1.327 | 0.184 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.135 | 1.956 | 2.314 | 1.000 | 23.329 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 8627.828 | 8742.342 | 0.966 | 0.041 | 0.048 |
# parameters of interest
params_lcs_imagi_ideal_frequ_hyp7 <- broom::tidy(fit_mi_lcs_imagi_ideal_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.042 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.651 | NA | NA |
| d_imagi_1 ~1 | 1.054 | 0.170 | 1.938 | 3.204 | 2.337 | 0.019 |
| imagi_t1 ~1 | 4.545 | 4.468 | 4.622 | 8.635 | 115.719 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.093 | 0.031 | 0.155 | 0.859 | 2.922 | 0.003 |
| imagi_t1 ~ frequ | 0.124 | 0.024 | 0.223 | 0.173 | 2.439 | 0.015 |
| d_imagi_1 ~ imagi_t1 | -0.234 | -0.424 | -0.045 | -0.375 | -2.422 | 0.015 |
| d_imagi_1 ~ frequ | 0.000 | -0.098 | 0.098 | -0.001 | -0.005 | 0.996 |
| frequ ~~ frequ | 0.544 | 0.366 | 0.723 | 1.000 | 5.977 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6874.112 | 6958.038 | 0.953 | 0.048 | 0.058 |
# parameters of interest
params_lcs_socia_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_socia_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t3 ~ socia_t2", "d_socia_1 =~ socia_t3", "d_socia_1 ~ socia_t2", # change parameters
"d_socia_1 ~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", "d_socia_1 ~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t2 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t3 ~ socia_t2 | 1.000 | 1.000 | 1.000 | 1.051 | NA | NA |
| d_socia_1 =~ socia_t3 | 1.000 | 1.000 | 1.000 | 0.624 | NA | NA |
| d_socia_1 ~1 | 0.863 | -0.028 | 1.755 | 3.696 | 1.899 | 0.058 |
| socia_t2 ~1 | 4.173 | 4.002 | 4.343 | 10.603 | 48.009 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.046 | 0.004 | 0.089 | 0.845 | 2.121 | 0.034 |
| d_socia_1 ~ socia_t2 | -0.222 | -0.427 | -0.017 | -0.374 | -2.125 | 0.034 |
| d_socia_1 ~ sa07_01_t1 | 0.019 | -0.016 | 0.055 | 0.116 | 1.088 | 0.277 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.925 | 1.713 | 2.137 | 1.000 | 17.799 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8264.175 | 8373.644 | 0.94 | 0.056 | 0.059 |
# parameters of interest
params_lcs_socia_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_socia_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t3 ~ socia_t2", "d_socia_1 =~ socia_t3", "d_socia_1 ~ socia_t2", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t2 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t2 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t3 ~ socia_t2 | 1.000 | 1.000 | 1.000 | 1.049 | NA | NA |
| d_socia_1 =~ socia_t3 | 1.000 | 1.000 | 1.000 | 0.619 | NA | NA |
| d_socia_1 ~1 | 0.934 | 0.008 | 1.860 | 3.961 | 1.976 | 0.048 |
| socia_t2 ~1 | 4.159 | 4.065 | 4.253 | 10.403 | 86.649 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.048 | 0.004 | 0.092 | 0.859 | 2.123 | 0.034 |
| socia_t2 ~ frequ | 0.081 | -0.030 | 0.191 | 0.149 | 1.433 | 0.152 |
| d_socia_1 ~ socia_t2 | -0.224 | -0.439 | -0.008 | -0.379 | -2.037 | 0.042 |
| d_socia_1 ~ frequ | 0.014 | -0.072 | 0.101 | 0.046 | 0.329 | 0.742 |
| frequ ~~ frequ | 0.550 | 0.371 | 0.729 | 1.000 | 6.036 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6957.508 | 7041.434 | 0.987 | 0.023 | 0.047 |
# parameters of interest
params_lcs_asser_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_asser_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t3 ~ asser_t2", "d_asser_1 =~ asser_t3", "d_asser_1 ~ asser_t2", # change parameters
"d_asser_1 ~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", "d_asser_1 ~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t2 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t3 ~ asser_t2 | 1.000 | 1.000 | 1.000 | 1.054 | NA | NA |
| d_asser_1 =~ asser_t3 | 1.000 | 1.000 | 1.000 | 0.797 | NA | NA |
| d_asser_1 ~1 | 1.356 | 0.633 | 2.080 | 4.579 | 3.674 | 0.000 |
| asser_t2 ~1 | 3.930 | 3.705 | 4.154 | 10.032 | 34.272 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.070 | 0.022 | 0.119 | 0.800 | 2.837 | 0.005 |
| d_asser_1 ~ asser_t2 | -0.342 | -0.519 | -0.166 | -0.453 | -3.798 | 0.000 |
| d_asser_1 ~ sa07_02_t1 | 0.010 | -0.032 | 0.052 | 0.046 | 0.484 | 0.628 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.767 | 1.578 | 1.955 | 1.000 | 18.385 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8372.446 | 8481.915 | 1 | 0 | 0.044 |
# parameters of interest
params_lcs_asser_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_asser_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t3 ~ asser_t2", "d_asser_1 =~ asser_t3", "d_asser_1 ~ asser_t2", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t2 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t2 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t3 ~ asser_t2 | 1.000 | 1.000 | 1.000 | 1.063 | NA | NA |
| d_asser_1 =~ asser_t3 | 1.000 | 1.000 | 1.000 | 0.800 | NA | NA |
| d_asser_1 ~1 | 1.366 | 0.577 | 2.154 | 4.459 | 3.395 | 0.001 |
| asser_t2 ~1 | 4.107 | 4.000 | 4.214 | 10.087 | 75.475 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.075 | 0.027 | 0.122 | 0.794 | 3.075 | 0.002 |
| asser_t2 ~ frequ | 0.140 | 0.006 | 0.275 | 0.256 | 2.053 | 0.040 |
| d_asser_1 ~ asser_t2 | -0.337 | -0.527 | -0.147 | -0.448 | -3.473 | 0.001 |
| d_asser_1 ~ frequ | -0.009 | -0.098 | 0.081 | -0.021 | -0.187 | 0.852 |
| frequ ~~ frequ | 0.550 | 0.374 | 0.726 | 1.000 | 6.131 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6588.092 | 6672.019 | 0.921 | 0.048 | 0.062 |
# parameters of interest
params_lcs_energ_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_energ_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t3 ~ energ_t2", "d_energ_1 =~ energ_t3", "d_energ_1 ~ energ_t2", # change parameters
"d_energ_1 ~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", "d_energ_1 ~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t2 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t3 ~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.971 | NA | NA |
| d_energ_1 =~ energ_t3 | 1.000 | 1.000 | 1.000 | 0.634 | NA | NA |
| d_energ_1 ~1 | 0.433 | -0.247 | 1.113 | 2.663 | 1.248 | 0.212 |
| energ_t2 ~1 | 2.266 | 2.118 | 2.414 | 9.093 | 30.084 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.024 | -0.017 | 0.065 | 0.918 | 1.164 | 0.244 |
| d_energ_1 ~ energ_t2 | -0.185 | -0.479 | 0.109 | -0.284 | -1.234 | 0.217 |
| d_energ_1 ~ sa07_03_t1 | -0.007 | -0.034 | 0.020 | -0.056 | -0.488 | 0.625 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.868 | 1.644 | 2.092 | 1.000 | 16.353 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 7975.067 | 8084.537 | 0.927 | 0.054 | 0.068 |
# parameters of interest
params_lcs_energ_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_energ_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t3 ~ energ_t2", "d_energ_1 =~ energ_t3", "d_energ_1 ~ energ_t2", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t2 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t2 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t3 ~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.958 | NA | NA |
| d_energ_1 =~ energ_t3 | 1.000 | 1.000 | 1.000 | 0.651 | NA | NA |
| d_energ_1 ~1 | 0.388 | -0.339 | 1.116 | 2.224 | 1.046 | 0.296 |
| energ_t2 ~1 | 2.232 | 2.106 | 2.357 | 8.679 | 34.905 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.028 | -0.014 | 0.071 | 0.923 | 1.294 | 0.196 |
| energ_t2 ~ frequ | -0.124 | -0.235 | -0.013 | -0.352 | -2.190 | 0.029 |
| d_energ_1 ~ energ_t2 | -0.175 | -0.507 | 0.157 | -0.257 | -1.030 | 0.303 |
| d_energ_1 ~ frequ | 0.012 | -0.060 | 0.083 | 0.048 | 0.317 | 0.751 |
| frequ ~~ frequ | 0.531 | 0.353 | 0.709 | 1.000 | 5.846 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 7022.316 | 7106.242 | 0.976 | 0.036 | 0.04 |
# parameters of interest
params_lcs_compa_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_compa_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t3 ~ compa_t2", "d_compa_1 =~ compa_t3", "d_compa_1 ~ compa_t2", # change parameters
"d_compa_1 ~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", "d_compa_1 ~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t2 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t3 ~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.986 | NA | NA |
| d_compa_1 =~ compa_t3 | 1.000 | 1.000 | 1.000 | 0.565 | NA | NA |
| d_compa_1 ~1 | 0.722 | -0.263 | 1.707 | 1.961 | 1.437 | 0.151 |
| compa_t2 ~1 | 4.529 | 4.362 | 4.697 | 7.048 | 53.033 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.126 | 0.025 | 0.226 | 0.928 | 2.457 | 0.014 |
| d_compa_1 ~ compa_t2 | -0.157 | -0.367 | 0.053 | -0.273 | -1.461 | 0.144 |
| d_compa_1 ~ sa07_04_t1 | -0.016 | -0.075 | 0.042 | -0.063 | -0.550 | 0.582 |
| sa07_04_t1 ~~ sa07_04_t1 | 2.021 | 1.779 | 2.262 | 1.000 | 16.389 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8404.887 | 8514.357 | 0.979 | 0.033 | 0.043 |
# parameters of interest
params_lcs_compa_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_compa_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t3 ~ compa_t2", "d_compa_1 =~ compa_t3", "d_compa_1 ~ compa_t2", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t2 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t2 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t3 ~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.989 | NA | NA |
| d_compa_1 =~ compa_t3 | 1.000 | 1.000 | 1.000 | 0.567 | NA | NA |
| d_compa_1 ~1 | 0.646 | -0.310 | 1.601 | 1.737 | 1.324 | 0.186 |
| compa_t2 ~1 | 4.336 | 4.232 | 4.440 | 6.690 | 81.798 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.128 | 0.025 | 0.231 | 0.926 | 2.433 | 0.015 |
| compa_t2 ~ frequ | 0.139 | 0.007 | 0.272 | 0.159 | 2.062 | 0.039 |
| d_compa_1 ~ compa_t2 | -0.148 | -0.360 | 0.064 | -0.258 | -1.369 | 0.171 |
| d_compa_1 ~ frequ | -0.028 | -0.161 | 0.106 | -0.055 | -0.408 | 0.683 |
| frequ ~~ frequ | 0.545 | 0.364 | 0.725 | 1.000 | 5.918 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 5452.331 | 5536.258 | 0.875 | 0.098 | 0.091 |
# parameters of interest
params_lcs_respe_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_respe_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t3 ~ respe_t2", "d_respe_1 =~ respe_t3", "d_respe_1 ~ respe_t2", # change parameters
"d_respe_1 ~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", "d_respe_1 ~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t2 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t3 ~ respe_t2 | 1.000 | 1.000 | 1.000 | 1.059 | NA | NA |
| d_respe_1 =~ respe_t3 | 1.000 | 1.000 | 1.000 | 0.423 | NA | NA |
| d_respe_1 ~1 | 0.601 | -0.002 | 1.203 | 4.172 | 1.955 | 0.051 |
| respe_t2 ~1 | 4.777 | 4.670 | 4.883 | 13.263 | 87.895 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.018 | -0.007 | 0.044 | 0.886 | 1.405 | 0.160 |
| d_respe_1 ~ respe_t2 | -0.132 | -0.257 | -0.006 | -0.330 | -2.059 | 0.040 |
| d_respe_1 ~ sa07_05_t1 | 0.004 | -0.022 | 0.030 | 0.036 | 0.290 | 0.772 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.738 | 1.433 | 2.043 | 1.000 | 11.166 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 6864.934 | 6974.403 | 0.939 | 0.065 | 0.071 |
# parameters of interest
params_lcs_respe_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_respe_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t3 ~ respe_t2", "d_respe_1 =~ respe_t3", "d_respe_1 ~ respe_t2", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t2 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t2 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t3 ~ respe_t2 | 1.000 | 1.000 | 1.000 | 1.052 | NA | NA |
| d_respe_1 =~ respe_t3 | 1.000 | 1.000 | 1.000 | 0.425 | NA | NA |
| d_respe_1 ~1 | 0.449 | -0.225 | 1.123 | 3.221 | 1.306 | 0.192 |
| respe_t2 ~1 | 4.684 | 4.621 | 4.747 | 13.574 | 145.043 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.016 | -0.007 | 0.039 | 0.830 | 1.383 | 0.167 |
| respe_t2 ~ frequ | 0.134 | 0.065 | 0.203 | 0.286 | 3.793 | 0.000 |
| d_respe_1 ~ respe_t2 | -0.098 | -0.240 | 0.044 | -0.242 | -1.355 | 0.175 |
| d_respe_1 ~ frequ | -0.051 | -0.109 | 0.007 | -0.272 | -1.734 | 0.083 |
| frequ ~~ frequ | 0.547 | 0.369 | 0.726 | 1.000 | 5.999 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6808.283 | 6892.21 | 0.974 | 0.036 | 0.043 |
# parameters of interest
params_lcs_trust_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_trust_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t3 ~ trust_t2", "d_trust_1 =~ trust_t3", "d_trust_1 ~ trust_t2", # change parameters
"d_trust_1 ~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", "d_trust_1 ~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t2 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t3 ~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.979 | NA | NA |
| d_trust_1 =~ trust_t3 | 1.000 | 1.000 | 1.000 | 0.480 | NA | NA |
| d_trust_1 ~1 | 0.188 | -0.144 | 0.520 | 0.953 | 1.109 | 0.267 |
| trust_t2 ~1 | 1.973 | 1.812 | 2.134 | 4.903 | 24.078 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.037 | -0.010 | 0.084 | 0.958 | 1.553 | 0.120 |
| d_trust_1 ~ trust_t2 | -0.098 | -0.263 | 0.068 | -0.200 | -1.159 | 0.247 |
| d_trust_1 ~ sa07_06_t1 | 0.006 | -0.033 | 0.046 | 0.043 | 0.308 | 0.758 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.903 | 1.686 | 2.120 | 1.000 | 17.178 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8187.363 | 8296.832 | 0.989 | 0.024 | 0.044 |
# parameters of interest
params_lcs_trust_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_trust_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t3 ~ trust_t2", "d_trust_1 =~ trust_t3", "d_trust_1 ~ trust_t2", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t2 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t2 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t3 ~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.981 | NA | NA |
| d_trust_1 =~ trust_t3 | 1.000 | 1.000 | 1.000 | 0.476 | NA | NA |
| d_trust_1 ~1 | 0.139 | -0.217 | 0.495 | 0.739 | 0.763 | 0.445 |
| trust_t2 ~1 | 1.951 | 1.847 | 2.054 | 5.046 | 36.858 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.032 | -0.012 | 0.077 | 0.923 | 1.446 | 0.148 |
| trust_t2 ~ frequ | -0.175 | -0.280 | -0.069 | -0.335 | -3.240 | 0.001 |
| d_trust_1 ~ trust_t2 | -0.065 | -0.250 | 0.120 | -0.134 | -0.690 | 0.490 |
| d_trust_1 ~ frequ | 0.051 | -0.032 | 0.134 | 0.201 | 1.201 | 0.230 |
| frequ ~~ frequ | 0.548 | 0.369 | 0.728 | 1.000 | 5.990 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 5925.007 | 6008.933 | 0.901 | 0.068 | 0.076 |
# parameters of interest
params_lcs_organ_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_organ_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t3 ~ organ_t2", "d_organ_1 =~ organ_t3", "d_organ_1 ~ organ_t2", # change parameters
"d_organ_1 ~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", "d_organ_1 ~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t2 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t3 ~ organ_t2 | 1.000 | 1.000 | 1.000 | 1.011 | NA | NA |
| d_organ_1 =~ organ_t3 | 1.000 | 1.000 | 1.000 | 0.588 | NA | NA |
| d_organ_1 ~1 | 0.308 | -0.025 | 0.642 | 1.174 | 1.813 | 0.070 |
| organ_t2 ~1 | 1.570 | 1.386 | 1.754 | 3.475 | 16.726 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.062 | -0.006 | 0.131 | 0.904 | 1.793 | 0.073 |
| d_organ_1 ~ organ_t2 | -0.179 | -0.402 | 0.045 | -0.307 | -1.567 | 0.117 |
| d_organ_1 ~ sa07_07_t1 | -0.004 | -0.045 | 0.036 | -0.025 | -0.217 | 0.828 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.101 | 1.901 | 2.301 | 1.000 | 20.565 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 7288.055 | 7397.524 | 0.943 | 0.053 | 0.066 |
# parameters of interest
params_lcs_organ_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_organ_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t3 ~ organ_t2", "d_organ_1 =~ organ_t3", "d_organ_1 ~ organ_t2", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t2 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t2 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t3 ~ organ_t2 | 1.000 | 1.000 | 1.000 | 1.013 | NA | NA |
| d_organ_1 =~ organ_t3 | 1.000 | 1.000 | 1.000 | 0.590 | NA | NA |
| d_organ_1 ~1 | 0.290 | -0.059 | 0.639 | 1.106 | 1.629 | 0.103 |
| organ_t2 ~1 | 1.666 | 1.563 | 1.769 | 3.703 | 31.645 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.062 | -0.006 | 0.130 | 0.900 | 1.778 | 0.075 |
| organ_t2 ~ frequ | -0.127 | -0.239 | -0.014 | -0.208 | -2.206 | 0.027 |
| d_organ_1 ~ organ_t2 | -0.176 | -0.403 | 0.051 | -0.302 | -1.517 | 0.129 |
| d_organ_1 ~ frequ | 0.018 | -0.067 | 0.104 | 0.052 | 0.423 | 0.672 |
| frequ ~~ frequ | 0.545 | 0.366 | 0.723 | 1.000 | 5.979 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 5246.662 | 5330.588 | 0.924 | 0.068 | 0.06 |
# parameters of interest
params_lcs_produ_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_produ_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t3 ~ produ_t2", "d_produ_1 =~ produ_t3", "d_produ_1 ~ produ_t2", # change parameters
"d_produ_1 ~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", "d_produ_1 ~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t2 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t3 ~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.933 | NA | NA |
| d_produ_1 =~ produ_t3 | 1.000 | 1.000 | 1.000 | 0.589 | NA | NA |
| d_produ_1 ~1 | 0.249 | -0.063 | 0.561 | 1.029 | 1.562 | 0.118 |
| produ_t2 ~1 | 1.386 | 1.254 | 1.517 | 3.615 | 20.684 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.056 | 0.005 | 0.108 | 0.961 | 2.134 | 0.033 |
| d_produ_1 ~ produ_t2 | -0.123 | -0.346 | 0.100 | -0.195 | -1.084 | 0.278 |
| d_produ_1 ~ sa07_08_t1 | -0.003 | -0.034 | 0.028 | -0.017 | -0.180 | 0.857 |
| sa07_08_t1 ~~ sa07_08_t1 | 1.945 | 1.739 | 2.151 | 1.000 | 18.494 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 6628.406 | 6737.875 | 0.934 | 0.063 | 0.06 |
# parameters of interest
params_lcs_produ_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_produ_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t3 ~ produ_t2", "d_produ_1 =~ produ_t3", "d_produ_1 ~ produ_t2", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t2 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t2 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t3 ~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.933 | NA | NA |
| d_produ_1 =~ produ_t3 | 1.000 | 1.000 | 1.000 | 0.588 | NA | NA |
| d_produ_1 ~1 | 0.211 | -0.130 | 0.552 | 0.867 | 1.215 | 0.224 |
| produ_t2 ~1 | 1.504 | 1.428 | 1.580 | 3.889 | 38.867 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.056 | 0.004 | 0.109 | 0.948 | 2.114 | 0.035 |
| produ_t2 ~ frequ | -0.140 | -0.234 | -0.046 | -0.265 | -2.917 | 0.004 |
| d_produ_1 ~ produ_t2 | -0.104 | -0.348 | 0.140 | -0.165 | -0.836 | 0.403 |
| d_produ_1 ~ frequ | 0.040 | -0.045 | 0.125 | 0.120 | 0.925 | 0.355 |
| frequ ~~ frequ | 0.534 | 0.355 | 0.712 | 1.000 | 5.863 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6202.454 | 6286.381 | 0.947 | 0.054 | 0.052 |
# parameters of interest
params_lcs_respo_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_respo_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t3 ~ respo_t2", "d_respo_1 =~ respo_t3", "d_respo_1 ~ respo_t2", # change parameters
"d_respo_1 ~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", "d_respo_1 ~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t2 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t3 ~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.991 | NA | NA |
| d_respo_1 =~ respo_t3 | 1.000 | 1.000 | 1.000 | 0.380 | NA | NA |
| d_respo_1 ~1 | 0.351 | -0.385 | 1.087 | 1.829 | 0.935 | 0.350 |
| respo_t2 ~1 | 4.429 | 4.270 | 4.588 | 8.843 | 54.611 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.035 | -0.019 | 0.089 | 0.953 | 1.270 | 0.204 |
| d_respo_1 ~ respo_t2 | -0.070 | -0.230 | 0.090 | -0.184 | -0.861 | 0.389 |
| d_respo_1 ~ sa07_09_t1 | -0.018 | -0.061 | 0.025 | -0.137 | -0.827 | 0.408 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.123 | 1.890 | 2.357 | 1.000 | 17.822 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 7560.493 | 7669.963 | 0.96 | 0.048 | 0.052 |
# parameters of interest
params_lcs_respo_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_respo_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t3 ~ respo_t2", "d_respo_1 =~ respo_t3", "d_respo_1 ~ respo_t2", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t2 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t2 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t3 ~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.990 | NA | NA |
| d_respo_1 =~ respo_t3 | 1.000 | 1.000 | 1.000 | 0.379 | NA | NA |
| d_respo_1 ~1 | 0.113 | -0.720 | 0.947 | 0.589 | 0.266 | 0.790 |
| respo_t2 ~1 | 4.326 | 4.220 | 4.432 | 8.609 | 79.971 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.031 | -0.025 | 0.087 | 0.846 | 1.091 | 0.275 |
| respo_t2 ~ frequ | 0.177 | 0.054 | 0.301 | 0.260 | 2.821 | 0.005 |
| d_respo_1 ~ respo_t2 | -0.026 | -0.213 | 0.160 | -0.068 | -0.274 | 0.784 |
| d_respo_1 ~ frequ | -0.097 | -0.210 | 0.017 | -0.370 | -1.669 | 0.095 |
| frequ ~~ frequ | 0.542 | 0.363 | 0.720 | 1.000 | 5.948 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6846.576 | 6930.502 | 0.943 | 0.044 | 0.055 |
# parameters of interest
params_lcs_anxie_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_anxie_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t3 ~ anxie_t2", "d_anxie_1 =~ anxie_t3", "d_anxie_1 ~ anxie_t2", # change parameters
"d_anxie_1 ~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", "d_anxie_1 ~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t2 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t3 ~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.915 | NA | NA |
| d_anxie_1 =~ anxie_t3 | 1.000 | 1.000 | 1.000 | 0.159 | NA | NA |
| d_anxie_1 ~1 | -0.488 | -1.888 | 0.913 | -12.263 | -0.683 | 0.495 |
| anxie_t2 ~1 | 4.587 | 4.476 | 4.698 | 19.994 | 81.000 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.000 | -0.021 | 0.022 | 0.135 | 0.019 | 0.984 |
| d_anxie_1 ~ anxie_t2 | 0.087 | -0.217 | 0.391 | 0.502 | 0.562 | 0.574 |
| d_anxie_1 ~ sa07_10_t1 | 0.022 | -0.002 | 0.046 | 0.800 | 1.771 | 0.077 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.149 | 1.932 | 2.366 | 1.000 | 19.408 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8209.513 | 8318.982 | 0.984 | 0.025 | 0.051 |
# parameters of interest
params_lcs_anxie_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_anxie_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t3 ~ anxie_t2", "d_anxie_1 =~ anxie_t3", "d_anxie_1 ~ anxie_t2", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t2 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t2 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t3 ~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.915 | NA | NA |
| d_anxie_1 =~ anxie_t3 | 1.000 | 1.000 | 1.000 | 0.153 | NA | NA |
| d_anxie_1 ~1 | -0.440 | -1.907 | 1.026 | -11.268 | -0.589 | 0.556 |
| anxie_t2 ~1 | 4.570 | 4.496 | 4.643 | 19.538 | 122.080 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.001 | -0.022 | 0.024 | 0.421 | 0.055 | 0.956 |
| anxie_t2 ~ frequ | 0.030 | -0.031 | 0.091 | 0.095 | 0.956 | 0.339 |
| d_anxie_1 ~ anxie_t2 | 0.093 | -0.225 | 0.410 | 0.555 | 0.572 | 0.567 |
| d_anxie_1 ~ frequ | -0.030 | -0.098 | 0.037 | -0.576 | -0.885 | 0.376 |
| frequ ~~ frequ | 0.547 | 0.368 | 0.726 | 1.000 | 5.986 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 5366.16 | 5450.086 | 0.883 | 0.077 | 0.07 |
# parameters of interest
params_lcs_depre_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_depre_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t3 ~ depre_t2", "d_depre_1 =~ depre_t3", "d_depre_1 ~ depre_t2", # change parameters
"d_depre_1 ~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", "d_depre_1 ~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t2 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t3 ~ depre_t2 | 1.000 | 1.000 | 1.000 | 1.029 | NA | NA |
| d_depre_1 =~ depre_t3 | 1.000 | 1.000 | 1.000 | 0.519 | NA | NA |
| d_depre_1 ~1 | 0.783 | -0.121 | 1.687 | 4.574 | 1.697 | 0.090 |
| depre_t2 ~1 | 4.479 | 4.353 | 4.605 | 13.207 | 69.685 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.026 | -0.006 | 0.058 | 0.886 | 1.600 | 0.110 |
| d_depre_1 ~ depre_t2 | -0.159 | -0.355 | 0.037 | -0.315 | -1.592 | 0.111 |
| d_depre_1 ~ sa07_11_t1 | -0.016 | -0.042 | 0.010 | -0.138 | -1.228 | 0.219 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.117 | 1.909 | 2.324 | 1.000 | 19.977 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 6729.933 | 6839.403 | 0.914 | 0.068 | 0.068 |
# parameters of interest
params_lcs_depre_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_depre_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t3 ~ depre_t2", "d_depre_1 =~ depre_t3", "d_depre_1 ~ depre_t2", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t2 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t2 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t3 ~ depre_t2 | 1.000 | 1.000 | 1.000 | 1.032 | NA | NA |
| d_depre_1 =~ depre_t3 | 1.000 | 1.000 | 1.000 | 0.520 | NA | NA |
| d_depre_1 ~1 | 0.661 | -0.267 | 1.588 | 3.755 | 1.396 | 0.163 |
| depre_t2 ~1 | 4.441 | 4.364 | 4.518 | 12.719 | 113.062 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.027 | -0.007 | 0.061 | 0.866 | 1.535 | 0.125 |
| depre_t2 ~ frequ | 0.070 | -0.012 | 0.152 | 0.147 | 1.669 | 0.095 |
| d_depre_1 ~ depre_t2 | -0.143 | -0.347 | 0.061 | -0.283 | -1.372 | 0.170 |
| d_depre_1 ~ frequ | -0.046 | -0.108 | 0.016 | -0.193 | -1.463 | 0.143 |
| frequ ~~ frequ | 0.542 | 0.363 | 0.720 | 1.000 | 5.960 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6286.275 | 6370.202 | 0.97 | 0.037 | 0.047 |
# parameters of interest
params_lcs_volat_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_volat_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t3 ~ volat_t2", "d_volat_1 =~ volat_t3", "d_volat_1 ~ volat_t2", # change parameters
"d_volat_1 ~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", "d_volat_1 ~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t2 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t3 ~ volat_t2 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_volat_1 =~ volat_t3 | 1.000 | 1.000 | 1.000 | 0.388 | NA | NA |
| d_volat_1 ~1 | 0.181 | -0.087 | 0.450 | 1.069 | 1.325 | 0.185 |
| volat_t2 ~1 | 1.526 | 1.378 | 1.674 | 3.435 | 20.199 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.027 | -0.017 | 0.072 | 0.944 | 1.197 | 0.231 |
| d_volat_1 ~ volat_t2 | -0.090 | -0.248 | 0.067 | -0.236 | -1.125 | 0.260 |
| d_volat_1 ~ sa07_12_t1 | 0.005 | -0.032 | 0.043 | 0.042 | 0.279 | 0.780 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.780 | 1.584 | 1.976 | 1.000 | 17.813 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 7696.29 | 7805.759 | 0.964 | 0.043 | 0.05 |
# parameters of interest
params_lcs_volat_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_volat_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t3 ~ volat_t2", "d_volat_1 =~ volat_t3", "d_volat_1 ~ volat_t2", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t2 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t2 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t3 ~ volat_t2 | 1.000 | 1.000 | 1.000 | 1.016 | NA | NA |
| d_volat_1 =~ volat_t3 | 1.000 | 1.000 | 1.000 | 0.385 | NA | NA |
| d_volat_1 ~1 | 0.172 | -0.090 | 0.434 | 1.024 | 1.285 | 0.199 |
| volat_t2 ~1 | 1.618 | 1.529 | 1.706 | 3.656 | 35.758 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.026 | -0.019 | 0.071 | 0.925 | 1.146 | 0.252 |
| volat_t2 ~ frequ | -0.129 | -0.226 | -0.032 | -0.216 | -2.614 | 0.009 |
| d_volat_1 ~ volat_t2 | -0.075 | -0.241 | 0.091 | -0.198 | -0.889 | 0.374 |
| d_volat_1 ~ frequ | 0.035 | -0.057 | 0.126 | 0.152 | 0.742 | 0.458 |
| frequ ~~ frequ | 0.543 | 0.365 | 0.722 | 1.000 | 5.960 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6693.246 | 6777.172 | 0.966 | 0.038 | 0.049 |
# parameters of interest
params_lcs_curio_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_curio_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t3 ~ curio_t2", "d_curio_1 =~ curio_t3", "d_curio_1 ~ curio_t2", # change parameters
"d_curio_1 ~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", "d_curio_1 ~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t2 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t3 ~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.909 | NA | NA |
| d_curio_1 =~ curio_t3 | 1.000 | 1.000 | 1.000 | 0.533 | NA | NA |
| d_curio_1 ~1 | 0.244 | -0.901 | 1.390 | 1.031 | 0.418 | 0.676 |
| curio_t2 ~1 | 4.637 | 4.501 | 4.773 | 11.483 | 66.727 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.055 | -0.003 | 0.114 | 0.985 | 1.856 | 0.063 |
| d_curio_1 ~ curio_t2 | -0.061 | -0.301 | 0.179 | -0.104 | -0.501 | 0.617 |
| d_curio_1 ~ sa07_13_t1 | 0.008 | -0.039 | 0.055 | 0.043 | 0.318 | 0.751 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.762 | 1.547 | 1.978 | 1.000 | 16.041 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8113.273 | 8222.742 | 0.99 | 0.022 | 0.046 |
# parameters of interest
params_lcs_curio_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_curio_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t3 ~ curio_t2", "d_curio_1 =~ curio_t3", "d_curio_1 ~ curio_t2", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t2 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t2 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t3 ~ curio_t2 | 1.000 | 1.000 | 1.000 | 0.914 | NA | NA |
| d_curio_1 =~ curio_t3 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_curio_1 ~1 | 0.389 | -0.671 | 1.449 | 1.643 | 0.719 | 0.472 |
| curio_t2 ~1 | 4.489 | 4.416 | 4.562 | 11.231 | 120.617 | 0.000 |
| d_curio_1 ~~ d_curio_1 | 0.053 | -0.003 | 0.109 | 0.944 | 1.840 | 0.066 |
| curio_t2 ~ frequ | 0.055 | -0.042 | 0.153 | 0.103 | 1.113 | 0.266 |
| d_curio_1 ~ curio_t2 | -0.089 | -0.320 | 0.142 | -0.151 | -0.756 | 0.450 |
| d_curio_1 ~ frequ | 0.063 | -0.028 | 0.154 | 0.198 | 1.365 | 0.172 |
| frequ ~~ frequ | 0.551 | 0.372 | 0.729 | 1.000 | 6.038 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6733.907 | 6817.833 | 1 | 0 | 0.036 |
# parameters of interest
params_lcs_aesth_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_aesth_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t3 ~ aesth_t2", "d_aesth_1 =~ aesth_t3", "d_aesth_1 ~ aesth_t2", # change parameters
"d_aesth_1 ~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", "d_aesth_1 ~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t2 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t3 ~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.950 | NA | NA |
| d_aesth_1 =~ aesth_t3 | 1.000 | 1.000 | 1.000 | 0.575 | NA | NA |
| d_aesth_1 ~1 | 0.364 | -0.056 | 0.784 | 2.246 | 1.700 | 0.089 |
| aesth_t2 ~1 | 2.592 | 2.384 | 2.799 | 9.662 | 24.514 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.024 | -0.003 | 0.052 | 0.929 | 1.736 | 0.083 |
| d_aesth_1 ~ aesth_t2 | -0.150 | -0.315 | 0.015 | -0.249 | -1.785 | 0.074 |
| d_aesth_1 ~ sa07_14_t1 | 0.018 | -0.006 | 0.043 | 0.162 | 1.474 | 0.141 |
| sa07_14_t1 ~~ sa07_14_t1 | 2.046 | 1.852 | 2.241 | 1.000 | 20.596 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 8109.692 | 8219.161 | 0.987 | 0.03 | 0.044 |
# parameters of interest
params_lcs_aesth_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_aesth_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t3 ~ aesth_t2", "d_aesth_1 =~ aesth_t3", "d_aesth_1 ~ aesth_t2", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t2 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t2 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t3 ~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.950 | NA | NA |
| d_aesth_1 =~ aesth_t3 | 1.000 | 1.000 | 1.000 | 0.577 | NA | NA |
| d_aesth_1 ~1 | 0.354 | -0.101 | 0.808 | 2.203 | 1.525 | 0.127 |
| aesth_t2 ~1 | 2.702 | 2.549 | 2.856 | 10.229 | 34.515 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.025 | -0.004 | 0.053 | 0.953 | 1.710 | 0.087 |
| aesth_t2 ~ frequ | -0.105 | -0.184 | -0.026 | -0.291 | -2.594 | 0.009 |
| d_aesth_1 ~ aesth_t2 | -0.128 | -0.301 | 0.046 | -0.210 | -1.441 | 0.149 |
| d_aesth_1 ~ frequ | 0.004 | -0.045 | 0.054 | 0.019 | 0.166 | 0.868 |
| frequ ~~ frequ | 0.539 | 0.360 | 0.717 | 1.000 | 5.905 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_specif_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 23 | 6371.892 | 6455.818 | 0.934 | 0.056 | 0.063 |
# parameters of interest
params_lcs_imagi_ideal_specif_hyp7_fu <- broom::tidy(fit_mi_lcs_imagi_ideal_specif_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t3 ~ imagi_t2", "d_imagi_1 =~ imagi_t3", "d_imagi_1 ~ imagi_t2", # change parameters
"d_imagi_1 ~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", "d_imagi_1 ~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t2 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_specif_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t3 ~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.944 | NA | NA |
| d_imagi_1 =~ imagi_t3 | 1.000 | 1.000 | 1.000 | 0.481 | NA | NA |
| d_imagi_1 ~1 | 0.231 | -1.075 | 1.536 | 0.958 | 0.346 | 0.729 |
| imagi_t2 ~1 | 4.732 | 4.588 | 4.876 | 10.023 | 64.402 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.056 | -0.008 | 0.120 | 0.969 | 1.717 | 0.086 |
| d_imagi_1 ~ imagi_t2 | -0.056 | -0.327 | 0.216 | -0.109 | -0.402 | 0.688 |
| d_imagi_1 ~ sa07_15_t1 | 0.019 | -0.026 | 0.063 | 0.115 | 0.827 | 0.408 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.182 | 1.988 | 2.376 | 1.000 | 22.048 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_ideal_frequ_hyp7_fu) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 284 | 30 | 7735.574 | 7845.043 | 0.976 | 0.034 | 0.052 |
# parameters of interest
params_lcs_imagi_ideal_frequ_hyp7_fu <- broom::tidy(fit_mi_lcs_imagi_ideal_frequ_hyp7_fu, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t3 ~ imagi_t2", "d_imagi_1 =~ imagi_t3", "d_imagi_1 ~ imagi_t2", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t2 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t2 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_ideal_frequ_hyp7_fu, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t3 ~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.942 | NA | NA |
| d_imagi_1 =~ imagi_t3 | 1.000 | 1.000 | 1.000 | 0.482 | NA | NA |
| d_imagi_1 ~1 | 0.348 | -0.928 | 1.624 | 1.437 | 0.535 | 0.593 |
| imagi_t2 ~1 | 4.514 | 4.434 | 4.594 | 9.520 | 110.076 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.058 | -0.008 | 0.123 | 0.982 | 1.721 | 0.085 |
| imagi_t2 ~ frequ | 0.091 | -0.004 | 0.186 | 0.142 | 1.872 | 0.061 |
| d_imagi_1 ~ imagi_t2 | -0.070 | -0.344 | 0.204 | -0.136 | -0.498 | 0.618 |
| d_imagi_1 ~ frequ | 0.011 | -0.097 | 0.118 | 0.032 | 0.195 | 0.846 |
| frequ ~~ frequ | 0.544 | 0.365 | 0.722 | 1.000 | 5.970 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7926.536 | 8014.329 | 0.961 | 0.073 | 0.042 |
# parameters of interest
params_lcs_socia_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_socia_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ sa07_01_t1", "sa07_01_t1 ~~ sa07_01_t1", "d_socia_1 ~ sa07_01_t1", # acceptance goals
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.076 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.479 | NA | NA |
| d_socia_1 ~1 | 0.651 | 0.278 | 1.023 | 1.478 | 3.424 | 0.001 |
| socia_t1 ~1 | 4.092 | 3.789 | 4.394 | 4.139 | 26.497 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.167 | 0.086 | 0.247 | 0.859 | 4.042 | 0.000 |
| d_socia_1 ~ socia_t1 | -0.169 | -0.244 | -0.093 | -0.379 | -4.361 | 0.000 |
| d_socia_1 ~ sa07_01_t1 | -0.002 | -0.060 | 0.056 | -0.006 | -0.063 | 0.950 |
| sa07_01_t1 ~~ sa07_01_t1 | 1.826 | 1.634 | 2.019 | 1.000 | 18.622 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_socia_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9235.151 | 9349.664 | 0.963 | 0.061 | 0.043 |
# parameters of interest
params_lcs_socia_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_socia_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("socia_t2 ~ socia_t1", "d_socia_1 =~ socia_t2", "d_socia_1 ~ socia_t1", # change parameters
"d_socia_1 ~ frequ", "frequ ~~ frequ", "socia_t1 ~ frequ", # frequency of self improvement
"d_socia_1 ~1 ", "socia_t1 ~1 ", "", # means
"d_socia_1 ~~ d_socia_1"))
kable(params_lcs_socia_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| socia_t2 ~ socia_t1 | 1.000 | 1.000 | 1.000 | 1.076 | NA | NA |
| d_socia_1 =~ socia_t2 | 1.000 | 1.000 | 1.000 | 0.478 | NA | NA |
| d_socia_1 ~1 | 0.636 | 0.436 | 0.836 | 1.460 | 6.235 | 0.000 |
| socia_t1 ~1 | 2.858 | 2.724 | 2.992 | 2.917 | 41.743 | 0.000 |
| d_socia_1 ~~ d_socia_1 | 0.163 | 0.083 | 0.243 | 0.858 | 3.986 | 0.000 |
| socia_t1 ~ frequ | 0.029 | -0.179 | 0.237 | 0.022 | 0.275 | 0.783 |
| d_socia_1 ~ socia_t1 | -0.166 | -0.231 | -0.102 | -0.374 | -5.068 | 0.000 |
| d_socia_1 ~ frequ | -0.020 | -0.142 | 0.102 | -0.034 | -0.321 | 0.748 |
| frequ ~~ frequ | 0.549 | 0.370 | 0.728 | 1.000 | 6.013 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8078.291 | 8166.085 | 0.983 | 0.041 | 0.039 |
# parameters of interest
params_lcs_asser_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_asser_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ sa07_02_t1", "sa07_02_t1 ~~ sa07_02_t1", "d_asser_1 ~ sa07_02_t1", # acceptance goals
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.372 | NA | NA |
| d_asser_1 ~1 | 0.199 | -0.139 | 0.537 | 0.728 | 1.153 | 0.249 |
| asser_t1 ~1 | 3.683 | 3.374 | 3.992 | 4.999 | 23.377 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.071 | 0.020 | 0.123 | 0.956 | 2.727 | 0.006 |
| d_asser_1 ~ asser_t1 | -0.062 | -0.147 | 0.022 | -0.169 | -1.453 | 0.146 |
| d_asser_1 ~ sa07_02_t1 | 0.017 | -0.029 | 0.062 | 0.081 | 0.723 | 0.470 |
| sa07_02_t1 ~~ sa07_02_t1 | 1.747 | 1.573 | 1.921 | 1.000 | 19.717 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_asser_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9341.854 | 9456.368 | 0.988 | 0.031 | 0.042 |
# parameters of interest
params_lcs_asser_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_asser_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("asser_t2 ~ asser_t1", "d_asser_1 =~ asser_t2", "d_asser_1 ~ asser_t1", # change parameters
"d_asser_1 ~ frequ", "frequ ~~ frequ", "asser_t1 ~ frequ", # frequency of self improvement
"d_asser_1 ~1 ", "asser_t1 ~1 ", "", # means
"d_asser_1 ~~ d_asser_1"))
kable(params_lcs_asser_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| asser_t2 ~ asser_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_asser_1 =~ asser_t2 | 1.000 | 1.000 | 1.000 | 0.369 | NA | NA |
| d_asser_1 ~1 | 0.280 | 0.039 | 0.522 | 1.076 | 2.278 | 0.023 |
| asser_t1 ~1 | 3.105 | 2.986 | 3.224 | 4.407 | 51.115 | 0.000 |
| d_asser_1 ~~ d_asser_1 | 0.063 | 0.017 | 0.109 | 0.926 | 2.680 | 0.007 |
| asser_t1 ~ frequ | 0.101 | -0.060 | 0.263 | 0.105 | 1.228 | 0.219 |
| d_asser_1 ~ asser_t1 | -0.073 | -0.151 | 0.005 | -0.197 | -1.824 | 0.068 |
| d_asser_1 ~ frequ | 0.074 | -0.020 | 0.169 | 0.208 | 1.546 | 0.122 |
| frequ ~~ frequ | 0.534 | 0.352 | 0.716 | 1.000 | 5.748 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8193.783 | 8281.577 | 0.987 | 0.034 | 0.045 |
# parameters of interest
params_lcs_energ_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_energ_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ sa07_03_t1", "sa07_03_t1 ~~ sa07_03_t1", "d_energ_1 ~ sa07_03_t1", # acceptance goals
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.018 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.488 | NA | NA |
| d_energ_1 ~1 | 0.306 | 0.047 | 0.564 | 1.703 | 2.317 | 0.021 |
| energ_t1 ~1 | 2.210 | 1.987 | 2.433 | 5.911 | 19.436 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.030 | 0.002 | 0.057 | 0.924 | 2.114 | 0.034 |
| d_energ_1 ~ energ_t1 | -0.130 | -0.241 | -0.019 | -0.270 | -2.288 | 0.022 |
| d_energ_1 ~ sa07_03_t1 | -0.001 | -0.029 | 0.026 | -0.011 | -0.099 | 0.921 |
| sa07_03_t1 ~~ sa07_03_t1 | 1.851 | 1.650 | 2.052 | 1.000 | 18.028 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_energ_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9491.901 | 9606.415 | 0.984 | 0.033 | 0.051 |
# parameters of interest
params_lcs_energ_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_energ_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("energ_t2 ~ energ_t1", "d_energ_1 =~ energ_t2", "d_energ_1 ~ energ_t1", # change parameters
"d_energ_1 ~ frequ", "frequ ~~ frequ", "energ_t1 ~ frequ", # frequency of self improvement
"d_energ_1 ~1 ", "energ_t1 ~1 ", "", # means
"d_energ_1 ~~ d_energ_1"))
kable(params_lcs_energ_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| energ_t2 ~ energ_t1 | 1.000 | 1.000 | 1.000 | 1.019 | NA | NA |
| d_energ_1 =~ energ_t2 | 1.000 | 1.000 | 1.000 | 0.487 | NA | NA |
| d_energ_1 ~1 | 0.324 | 0.063 | 0.585 | 1.757 | 2.437 | 0.015 |
| energ_t1 ~1 | 2.676 | 2.564 | 2.789 | 6.934 | 46.622 | 0.000 |
| d_energ_1 ~~ d_energ_1 | 0.031 | 0.001 | 0.060 | 0.903 | 2.060 | 0.039 |
| energ_t1 ~ frequ | -0.048 | -0.142 | 0.046 | -0.091 | -0.998 | 0.318 |
| d_energ_1 ~ energ_t1 | -0.139 | -0.237 | -0.041 | -0.290 | -2.779 | 0.005 |
| d_energ_1 ~ frequ | -0.036 | -0.088 | 0.017 | -0.143 | -1.333 | 0.183 |
| frequ ~~ frequ | 0.543 | 0.365 | 0.720 | 1.000 | 5.994 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8053.413 | 8141.206 | 0.972 | 0.045 | 0.045 |
# parameters of interest
params_lcs_compa_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_compa_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ sa07_04_t1", "sa07_04_t1 ~~ sa07_04_t1", "d_compa_1 ~ sa07_04_t1", # acceptance goals
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.893 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.554 | NA | NA |
| d_compa_1 ~1 | 0.531 | -0.332 | 1.394 | 1.396 | 1.206 | 0.228 |
| compa_t1 ~1 | 4.546 | 4.397 | 4.694 | 7.408 | 60.131 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.141 | 0.049 | 0.232 | 0.971 | 3.024 | 0.002 |
| d_compa_1 ~ compa_t1 | -0.094 | -0.273 | 0.085 | -0.151 | -1.026 | 0.305 |
| d_compa_1 ~ sa07_04_t1 | -0.038 | -0.104 | 0.028 | -0.141 | -1.122 | 0.262 |
| sa07_04_t1 ~~ sa07_04_t1 | 2.024 | 1.813 | 2.235 | 1.000 | 18.799 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_compa_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9270.387 | 9384.9 | 0.973 | 0.041 | 0.05 |
# parameters of interest
params_lcs_compa_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_compa_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("compa_t2 ~ compa_t1", "d_compa_1 =~ compa_t2", "d_compa_1 ~ compa_t1", # change parameters
"d_compa_1 ~ frequ", "frequ ~~ frequ", "compa_t1 ~ frequ", # frequency of self improvement
"d_compa_1 ~1 ", "compa_t1 ~1 ", "", # means
"d_compa_1 ~~ d_compa_1"))
kable(params_lcs_compa_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| compa_t2 ~ compa_t1 | 1.000 | 1.000 | 1.000 | 0.897 | NA | NA |
| d_compa_1 =~ compa_t2 | 1.000 | 1.000 | 1.000 | 0.551 | NA | NA |
| d_compa_1 ~1 | 0.322 | -0.396 | 1.039 | 0.820 | 0.878 | 0.380 |
| compa_t1 ~1 | 4.213 | 4.123 | 4.303 | 6.594 | 91.762 | 0.000 |
| d_compa_1 ~~ d_compa_1 | 0.152 | 0.050 | 0.254 | 0.988 | 2.911 | 0.004 |
| compa_t1 ~ frequ | 0.172 | 0.036 | 0.307 | 0.197 | 2.478 | 0.013 |
| d_compa_1 ~ compa_t1 | -0.064 | -0.227 | 0.099 | -0.105 | -0.772 | 0.440 |
| d_compa_1 ~ frequ | -0.012 | -0.136 | 0.113 | -0.022 | -0.182 | 0.856 |
| frequ ~~ frequ | 0.540 | 0.360 | 0.720 | 1.000 | 5.888 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 6959.501 | 7047.295 | 0.967 | 0.052 | 0.054 |
# parameters of interest
params_lcs_respe_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_respe_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ sa07_05_t1", "sa07_05_t1 ~~ sa07_05_t1", "d_respe_1 ~ sa07_05_t1", # acceptance goals
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.999 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_respe_1 ~1 | 1.468 | 0.661 | 2.275 | 4.458 | 3.567 | 0.000 |
| respe_t1 ~1 | 4.734 | 4.616 | 4.852 | 9.044 | 78.428 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.084 | 0.025 | 0.144 | 0.779 | 2.803 | 0.005 |
| d_respe_1 ~ respe_t1 | -0.283 | -0.449 | -0.118 | -0.451 | -3.364 | 0.001 |
| d_respe_1 ~ sa07_05_t1 | -0.094 | -0.152 | -0.035 | -0.376 | -3.135 | 0.002 |
| sa07_05_t1 ~~ sa07_05_t1 | 1.744 | 1.473 | 2.015 | 1.000 | 12.613 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respe_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 8263.913 | 8378.426 | 0.965 | 0.049 | 0.065 |
# parameters of interest
params_lcs_respe_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respe_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respe_t2 ~ respe_t1", "d_respe_1 =~ respe_t2", "d_respe_1 ~ respe_t1", # change parameters
"d_respe_1 ~ frequ", "frequ ~~ frequ", "respe_t1 ~ frequ", # frequency of self improvement
"d_respe_1 ~1 ", "respe_t1 ~1 ", "", # means
"d_respe_1 ~~ d_respe_1"))
kable(params_lcs_respe_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respe_t2 ~ respe_t1 | 1.000 | 1.000 | 1.000 | 0.997 | NA | NA |
| d_respe_1 =~ respe_t2 | 1.000 | 1.000 | 1.000 | 0.629 | NA | NA |
| d_respe_1 ~1 | 0.919 | 0.152 | 1.686 | 2.776 | 2.347 | 0.019 |
| respe_t1 ~1 | 4.444 | 4.374 | 4.514 | 8.477 | 124.257 | 0.000 |
| d_respe_1 ~~ d_respe_1 | 0.098 | 0.024 | 0.171 | 0.890 | 2.588 | 0.010 |
| respe_t1 ~ frequ | 0.053 | -0.056 | 0.161 | 0.074 | 0.955 | 0.340 |
| d_respe_1 ~ respe_t1 | -0.201 | -0.369 | -0.034 | -0.319 | -2.357 | 0.018 |
| d_respe_1 ~ frequ | 0.052 | -0.033 | 0.136 | 0.115 | 1.201 | 0.230 |
| frequ ~~ frequ | 0.543 | 0.365 | 0.721 | 1.000 | 5.972 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8321.353 | 8409.146 | 0.966 | 0.051 | 0.049 |
# parameters of interest
params_lcs_trust_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_trust_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ sa07_06_t1", "sa07_06_t1 ~~ sa07_06_t1", "d_trust_1 ~ sa07_06_t1", # acceptance goals
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.040 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.314 | NA | NA |
| d_trust_1 ~1 | 0.199 | -0.114 | 0.512 | 0.928 | 1.245 | 0.213 |
| trust_t1 ~1 | 2.489 | 2.274 | 2.705 | 3.508 | 22.676 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.037 | -0.030 | 0.104 | 0.804 | 1.075 | 0.282 |
| d_trust_1 ~ trust_t1 | -0.053 | -0.168 | 0.062 | -0.177 | -0.910 | 0.363 |
| d_trust_1 ~ sa07_06_t1 | -0.056 | -0.110 | -0.002 | -0.360 | -2.034 | 0.042 |
| sa07_06_t1 ~~ sa07_06_t1 | 1.909 | 1.711 | 2.108 | 1.000 | 18.829 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_trust_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9551.319 | 9665.832 | 0.963 | 0.049 | 0.063 |
# parameters of interest
params_lcs_trust_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_trust_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("trust_t2 ~ trust_t1", "d_trust_1 =~ trust_t2", "d_trust_1 ~ trust_t1", # change parameters
"d_trust_1 ~ frequ", "frequ ~~ frequ", "trust_t1 ~ frequ", # frequency of self improvement
"d_trust_1 ~1 ", "trust_t1 ~1 ", "", # means
"d_trust_1 ~~ d_trust_1"))
kable(params_lcs_trust_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| trust_t2 ~ trust_t1 | 1.000 | 1.000 | 1.000 | 1.038 | NA | NA |
| d_trust_1 =~ trust_t2 | 1.000 | 1.000 | 1.000 | 0.312 | NA | NA |
| d_trust_1 ~1 | 0.150 | -0.166 | 0.465 | 0.706 | 0.930 | 0.352 |
| trust_t1 ~1 | 2.849 | 2.732 | 2.967 | 4.035 | 47.461 | 0.000 |
| d_trust_1 ~~ d_trust_1 | 0.040 | -0.027 | 0.107 | 0.895 | 1.181 | 0.238 |
| trust_t1 ~ frequ | -0.083 | -0.242 | 0.076 | -0.086 | -1.020 | 0.308 |
| d_trust_1 ~ trust_t1 | -0.086 | -0.194 | 0.023 | -0.285 | -1.550 | 0.121 |
| d_trust_1 ~ frequ | -0.052 | -0.154 | 0.050 | -0.179 | -0.991 | 0.322 |
| frequ ~~ frequ | 0.541 | 0.363 | 0.719 | 1.000 | 5.946 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7915.149 | 8002.942 | 1 | 0 | 0.023 |
# parameters of interest
params_lcs_organ_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_organ_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ sa07_07_t1", "sa07_07_t1 ~~ sa07_07_t1", "d_organ_1 ~ sa07_07_t1", # acceptance goals
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.507 | NA | NA |
| d_organ_1 ~1 | 0.297 | 0.037 | 0.558 | 0.628 | 2.237 | 0.025 |
| organ_t1 ~1 | 1.826 | 1.549 | 2.103 | 1.950 | 12.916 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.205 | 0.108 | 0.302 | 0.914 | 4.127 | 0.000 |
| d_organ_1 ~ organ_t1 | -0.179 | -0.293 | -0.065 | -0.355 | -3.087 | 0.002 |
| d_organ_1 ~ sa07_07_t1 | 0.055 | -0.012 | 0.122 | 0.167 | 1.602 | 0.109 |
| sa07_07_t1 ~~ sa07_07_t1 | 2.070 | 1.888 | 2.251 | 1.000 | 22.344 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_organ_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9211.576 | 9326.089 | 1 | 0 | 0.029 |
# parameters of interest
params_lcs_organ_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_organ_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("organ_t2 ~ organ_t1", "d_organ_1 =~ organ_t2", "d_organ_1 ~ organ_t1", # change parameters
"d_organ_1 ~ frequ", "frequ ~~ frequ", "organ_t1 ~ frequ", # frequency of self improvement
"d_organ_1 ~1 ", "organ_t1 ~1 ", "", # means
"d_organ_1 ~~ d_organ_1"))
kable(params_lcs_organ_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| organ_t2 ~ organ_t1 | 1.000 | 1.000 | 1.000 | 1.003 | NA | NA |
| d_organ_1 =~ organ_t2 | 1.000 | 1.000 | 1.000 | 0.509 | NA | NA |
| d_organ_1 ~1 | 0.342 | 0.077 | 0.608 | 0.736 | 2.524 | 0.012 |
| organ_t1 ~1 | 2.963 | 2.828 | 3.098 | 3.229 | 43.056 | 0.000 |
| d_organ_1 ~~ d_organ_1 | 0.201 | 0.104 | 0.297 | 0.926 | 4.063 | 0.000 |
| organ_t1 ~ frequ | -0.180 | -0.377 | 0.017 | -0.144 | -1.788 | 0.074 |
| d_organ_1 ~ organ_t1 | -0.137 | -0.227 | -0.048 | -0.271 | -3.017 | 0.003 |
| d_organ_1 ~ frequ | -0.050 | -0.172 | 0.072 | -0.079 | -0.801 | 0.423 |
| frequ ~~ frequ | 0.543 | 0.365 | 0.722 | 1.000 | 5.969 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7795.129 | 7882.923 | 0.966 | 0.061 | 0.043 |
# parameters of interest
params_lcs_produ_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_produ_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ sa07_08_t1", "sa07_08_t1 ~~ sa07_08_t1", "d_produ_1 ~ sa07_08_t1", # acceptance goals
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.962 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.540 | NA | NA |
| d_produ_1 ~1 | 0.279 | -0.055 | 0.614 | 0.708 | 1.635 | 0.102 |
| produ_t1 ~1 | 2.428 | 2.131 | 2.725 | 3.453 | 16.029 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.149 | 0.078 | 0.220 | 0.956 | 4.138 | 0.000 |
| d_produ_1 ~ produ_t1 | -0.125 | -0.257 | 0.008 | -0.222 | -1.837 | 0.066 |
| d_produ_1 ~ sa07_08_t1 | 0.006 | -0.061 | 0.073 | 0.022 | 0.185 | 0.853 |
| sa07_08_t1 ~~ sa07_08_t1 | 1.880 | 1.691 | 2.069 | 1.000 | 19.477 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_produ_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9108.876 | 9223.39 | 0.968 | 0.051 | 0.051 |
# parameters of interest
params_lcs_produ_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_produ_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("produ_t2 ~ produ_t1", "d_produ_1 =~ produ_t2", "d_produ_1 ~ produ_t1", # change parameters
"d_produ_1 ~ frequ", "frequ ~~ frequ", "produ_t1 ~ frequ", # frequency of self improvement
"d_produ_1 ~1 ", "produ_t1 ~1 ", "", # means
"d_produ_1 ~~ d_produ_1"))
kable(params_lcs_produ_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| produ_t2 ~ produ_t1 | 1.000 | 1.000 | 1.000 | 0.960 | NA | NA |
| d_produ_1 =~ produ_t2 | 1.000 | 1.000 | 1.000 | 0.541 | NA | NA |
| d_produ_1 ~1 | 0.320 | -0.015 | 0.654 | 0.814 | 1.871 | 0.061 |
| produ_t1 ~1 | 3.428 | 3.300 | 3.556 | 4.921 | 52.346 | 0.000 |
| d_produ_1 ~~ d_produ_1 | 0.143 | 0.073 | 0.212 | 0.926 | 4.040 | 0.000 |
| produ_t1 ~ frequ | -0.135 | -0.293 | 0.024 | -0.141 | -1.668 | 0.095 |
| d_produ_1 ~ produ_t1 | -0.130 | -0.228 | -0.032 | -0.231 | -2.600 | 0.009 |
| d_produ_1 ~ frequ | -0.097 | -0.212 | 0.017 | -0.181 | -1.668 | 0.095 |
| frequ ~~ frequ | 0.533 | 0.352 | 0.715 | 1.000 | 5.764 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7947.26 | 8035.053 | 0.893 | 0.099 | 0.085 |
# parameters of interest
params_lcs_respo_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_respo_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ sa07_09_t1", "sa07_09_t1 ~~ sa07_09_t1", "d_respo_1 ~ sa07_09_t1", # acceptance goals
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.017 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.509 | NA | NA |
| d_respo_1 ~1 | 0.551 | 0.029 | 1.074 | 3.055 | 2.068 | 0.039 |
| respo_t1 ~1 | 3.994 | 3.819 | 4.170 | 11.074 | 44.576 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.030 | 0.001 | 0.059 | 0.920 | 2.048 | 0.041 |
| d_respo_1 ~ respo_t1 | -0.144 | -0.270 | -0.019 | -0.289 | -2.253 | 0.024 |
| d_respo_1 ~ sa07_09_t1 | -0.001 | -0.033 | 0.031 | -0.010 | -0.081 | 0.936 |
| sa07_09_t1 ~~ sa07_09_t1 | 2.069 | 1.860 | 2.278 | 1.000 | 19.415 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_respo_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9213.842 | 9328.355 | 0.895 | 0.087 | 0.088 |
# parameters of interest
params_lcs_respo_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_respo_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("respo_t2 ~ respo_t1", "d_respo_1 =~ respo_t2", "d_respo_1 ~ respo_t1", # change parameters
"d_respo_1 ~ frequ", "frequ ~~ frequ", "respo_t1 ~ frequ", # frequency of self improvement
"d_respo_1 ~1 ", "respo_t1 ~1 ", "", # means
"d_respo_1 ~~ d_respo_1"))
kable(params_lcs_respo_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| respo_t2 ~ respo_t1 | 1.000 | 1.000 | 1.000 | 1.015 | NA | NA |
| d_respo_1 =~ respo_t2 | 1.000 | 1.000 | 1.000 | 0.507 | NA | NA |
| d_respo_1 ~1 | 0.528 | 0.167 | 0.889 | 2.878 | 2.864 | 0.004 |
| respo_t1 ~1 | 3.659 | 3.557 | 3.760 | 9.957 | 70.684 | 0.000 |
| d_respo_1 ~~ d_respo_1 | 0.031 | 0.001 | 0.061 | 0.922 | 2.017 | 0.044 |
| respo_t1 ~ frequ | 0.028 | -0.061 | 0.116 | 0.056 | 0.617 | 0.537 |
| d_respo_1 ~ respo_t1 | -0.139 | -0.235 | -0.043 | -0.278 | -2.831 | 0.005 |
| d_respo_1 ~ frequ | -0.005 | -0.053 | 0.044 | -0.020 | -0.196 | 0.844 |
| frequ ~~ frequ | 0.549 | 0.371 | 0.728 | 1.000 | 6.019 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8155.146 | 8242.94 | 1 | 0 | 0.027 |
# parameters of interest
params_lcs_anxie_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_anxie_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ sa07_10_t1", "sa07_10_t1 ~~ sa07_10_t1", "d_anxie_1 ~ sa07_10_t1", # acceptance goals
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.983 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.430 | NA | NA |
| d_anxie_1 ~1 | 0.607 | 0.191 | 1.022 | 1.480 | 2.864 | 0.004 |
| anxie_t1 ~1 | 3.815 | 3.514 | 4.116 | 4.071 | 24.832 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.160 | 0.072 | 0.249 | 0.954 | 3.554 | 0.000 |
| d_anxie_1 ~ anxie_t1 | -0.103 | -0.201 | -0.005 | -0.236 | -2.069 | 0.039 |
| d_anxie_1 ~ sa07_10_t1 | -0.037 | -0.097 | 0.023 | -0.132 | -1.209 | 0.227 |
| sa07_10_t1 ~~ sa07_10_t1 | 2.115 | 1.921 | 2.308 | 1.000 | 21.431 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_anxie_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9385.887 | 9500.4 | 0.99 | 0.028 | 0.043 |
# parameters of interest
params_lcs_anxie_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_anxie_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("anxie_t2 ~ anxie_t1", "d_anxie_1 =~ anxie_t2", "d_anxie_1 ~ anxie_t1", # change parameters
"d_anxie_1 ~ frequ", "frequ ~~ frequ", "anxie_t1 ~ frequ", # frequency of self improvement
"d_anxie_1 ~1 ", "anxie_t1 ~1 ", "", # means
"d_anxie_1 ~~ d_anxie_1"))
kable(params_lcs_anxie_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| anxie_t2 ~ anxie_t1 | 1.000 | 1.000 | 1.000 | 0.982 | NA | NA |
| d_anxie_1 =~ anxie_t2 | 1.000 | 1.000 | 1.000 | 0.428 | NA | NA |
| d_anxie_1 ~1 | 0.346 | 0.083 | 0.608 | 0.834 | 2.579 | 0.010 |
| anxie_t1 ~1 | 2.876 | 2.745 | 3.008 | 3.027 | 42.816 | 0.000 |
| d_anxie_1 ~~ d_anxie_1 | 0.153 | 0.062 | 0.244 | 0.888 | 3.286 | 0.001 |
| anxie_t1 ~ frequ | -0.223 | -0.395 | -0.050 | -0.176 | -2.529 | 0.011 |
| d_anxie_1 ~ anxie_t1 | -0.054 | -0.147 | 0.038 | -0.125 | -1.152 | 0.249 |
| d_anxie_1 ~ frequ | 0.160 | 0.026 | 0.295 | 0.290 | 2.332 | 0.020 |
| frequ ~~ frequ | 0.563 | 0.381 | 0.745 | 1.000 | 6.055 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8058.315 | 8146.108 | 0.954 | 0.077 | 0.06 |
# parameters of interest
params_lcs_depre_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_depre_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ sa07_11_t1", "sa07_11_t1 ~~ sa07_11_t1", "d_depre_1 ~ sa07_11_t1", # acceptance goals
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.987 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.508 | NA | NA |
| d_depre_1 ~1 | 0.575 | 0.259 | 0.890 | 2.010 | 3.571 | 0.000 |
| depre_t1 ~1 | 3.571 | 3.375 | 3.767 | 6.424 | 35.659 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.077 | 0.042 | 0.112 | 0.943 | 4.306 | 0.000 |
| d_depre_1 ~ depre_t1 | -0.135 | -0.221 | -0.048 | -0.262 | -3.036 | 0.002 |
| d_depre_1 ~ sa07_11_t1 | -0.013 | -0.045 | 0.019 | -0.066 | -0.785 | 0.432 |
| sa07_11_t1 ~~ sa07_11_t1 | 2.106 | 1.918 | 2.294 | 1.000 | 21.989 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_depre_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9302.558 | 9417.072 | 0.955 | 0.066 | 0.066 |
# parameters of interest
params_lcs_depre_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_depre_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("depre_t2 ~ depre_t1", "d_depre_1 =~ depre_t2", "d_depre_1 ~ depre_t1", # change parameters
"d_depre_1 ~ frequ", "frequ ~~ frequ", "depre_t1 ~ frequ", # frequency of self improvement
"d_depre_1 ~1 ", "depre_t1 ~1 ", "", # means
"d_depre_1 ~~ d_depre_1"))
kable(params_lcs_depre_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| depre_t2 ~ depre_t1 | 1.000 | 1.000 | 1.000 | 0.985 | NA | NA |
| d_depre_1 =~ depre_t2 | 1.000 | 1.000 | 1.000 | 0.505 | NA | NA |
| d_depre_1 ~1 | 0.437 | 0.203 | 0.672 | 1.538 | 3.656 | 0.000 |
| depre_t1 ~1 | 3.025 | 2.908 | 3.142 | 5.448 | 50.721 | 0.000 |
| d_depre_1 ~~ d_depre_1 | 0.073 | 0.039 | 0.106 | 0.900 | 4.268 | 0.000 |
| depre_t1 ~ frequ | -0.089 | -0.199 | 0.020 | -0.119 | -1.594 | 0.111 |
| d_depre_1 ~ depre_t1 | -0.103 | -0.180 | -0.025 | -0.200 | -2.599 | 0.009 |
| d_depre_1 ~ frequ | 0.085 | 0.008 | 0.162 | 0.222 | 2.172 | 0.030 |
| frequ ~~ frequ | 0.550 | 0.370 | 0.730 | 1.000 | 5.985 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8162.721 | 8250.515 | 0.99 | 0.032 | 0.037 |
# parameters of interest
params_lcs_volat_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_volat_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ sa07_12_t1", "sa07_12_t1 ~~ sa07_12_t1", "d_volat_1 ~ sa07_12_t1", # acceptance goals
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.057 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.452 | NA | NA |
| d_volat_1 ~1 | 0.330 | 0.073 | 0.588 | 0.815 | 2.516 | 0.012 |
| volat_t1 ~1 | 2.236 | 1.963 | 2.508 | 2.357 | 16.086 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.146 | 0.063 | 0.228 | 0.885 | 3.460 | 0.001 |
| d_volat_1 ~ volat_t1 | -0.134 | -0.233 | -0.035 | -0.314 | -2.653 | 0.008 |
| d_volat_1 ~ sa07_12_t1 | -0.016 | -0.087 | 0.056 | -0.050 | -0.428 | 0.668 |
| sa07_12_t1 ~~ sa07_12_t1 | 1.709 | 1.531 | 1.887 | 1.000 | 18.847 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_volat_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9468.703 | 9583.217 | 0.985 | 0.035 | 0.045 |
# parameters of interest
params_lcs_volat_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_volat_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("volat_t2 ~ volat_t1", "d_volat_1 =~ volat_t2", "d_volat_1 ~ volat_t1", # change parameters
"d_volat_1 ~ frequ", "frequ ~~ frequ", "volat_t1 ~ frequ", # frequency of self improvement
"d_volat_1 ~1 ", "volat_t1 ~1 ", "", # means
"d_volat_1 ~~ d_volat_1"))
kable(params_lcs_volat_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| volat_t2 ~ volat_t1 | 1.000 | 1.000 | 1.000 | 1.056 | NA | NA |
| d_volat_1 =~ volat_t2 | 1.000 | 1.000 | 1.000 | 0.450 | NA | NA |
| d_volat_1 ~1 | 0.313 | 0.051 | 0.575 | 0.771 | 2.342 | 0.019 |
| volat_t1 ~1 | 3.119 | 2.986 | 3.251 | 3.272 | 45.993 | 0.000 |
| d_volat_1 ~~ d_volat_1 | 0.146 | 0.064 | 0.229 | 0.887 | 3.471 | 0.001 |
| volat_t1 ~ frequ | 0.068 | -0.118 | 0.254 | 0.053 | 0.714 | 0.475 |
| d_volat_1 ~ volat_t1 | -0.143 | -0.224 | -0.062 | -0.336 | -3.479 | 0.001 |
| d_volat_1 ~ frequ | 0.018 | -0.106 | 0.141 | 0.032 | 0.279 | 0.780 |
| frequ ~~ frequ | 0.555 | 0.373 | 0.737 | 1.000 | 5.976 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7851.661 | 7939.454 | 0.982 | 0.038 | 0.047 |
# parameters of interest
params_lcs_curio_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_curio_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ sa07_13_t1", "sa07_13_t1 ~~ sa07_13_t1", "d_curio_1 ~ sa07_13_t1", # acceptance goals
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.085 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | NA | NA | NA |
| d_curio_1 ~1 | 0.073 | -0.424 | 0.570 | NA | 0.287 | 0.774 |
| curio_t1 ~1 | 4.376 | 4.210 | 4.542 | 7.657 | 51.627 | 0.000 |
| d_curio_1 ~~ d_curio_1 | -0.016 | -0.053 | 0.021 | NA | -0.828 | 0.408 |
| d_curio_1 ~ curio_t1 | -0.026 | -0.136 | 0.084 | NA | -0.464 | 0.643 |
| d_curio_1 ~ sa07_13_t1 | 0.040 | 0.004 | 0.075 | NA | 2.202 | 0.028 |
| sa07_13_t1 ~~ sa07_13_t1 | 1.724 | 1.530 | 1.918 | 1.000 | 17.438 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_curio_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9121.411 | 9235.924 | 0.991 | 0.025 | 0.04 |
# parameters of interest
params_lcs_curio_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_curio_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("curio_t2 ~ curio_t1", "d_curio_1 =~ curio_t2", "d_curio_1 ~ curio_t1", # change parameters
"d_curio_1 ~ frequ", "frequ ~~ frequ", "curio_t1 ~ frequ", # frequency of self improvement
"d_curio_1 ~1 ", "curio_t1 ~1 ", "", # means
"d_curio_1 ~~ d_curio_1"))
kable(params_lcs_curio_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| curio_t2 ~ curio_t1 | 1.000 | 1.000 | 1.000 | 1.084 | NA | NA |
| d_curio_1 =~ curio_t2 | 1.000 | 1.000 | 1.000 | NA | NA | NA |
| d_curio_1 ~1 | 0.315 | -0.106 | 0.736 | NA | 1.466 | 0.143 |
| curio_t1 ~1 | 4.032 | 3.933 | 4.131 | 7.155 | 79.542 | 0.000 |
| d_curio_1 ~~ d_curio_1 | -0.015 | -0.049 | 0.019 | NA | -0.851 | 0.394 |
| curio_t1 ~ frequ | 0.089 | -0.024 | 0.201 | 0.116 | 1.539 | 0.124 |
| d_curio_1 ~ curio_t1 | -0.063 | -0.165 | 0.038 | NA | -1.225 | 0.220 |
| d_curio_1 ~ frequ | 0.053 | -0.019 | 0.125 | NA | 1.449 | 0.147 |
| frequ ~~ frequ | 0.541 | 0.364 | 0.719 | 1.000 | 5.972 | 0.000 |
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 8194.758 | 8282.551 | 1 | 0 | 0.037 |
# parameters of interest
params_lcs_aesth_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_aesth_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ sa07_14_t1", "sa07_14_t1 ~~ sa07_14_t1", "d_aesth_1 ~ sa07_14_t1", # acceptance goals
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.996 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.377 | NA | NA |
| d_aesth_1 ~1 | 0.206 | -0.062 | 0.474 | 1.594 | 1.506 | 0.132 |
| aesth_t1 ~1 | 2.615 | 2.433 | 2.798 | 7.680 | 28.028 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.016 | -0.005 | 0.037 | 0.959 | 1.504 | 0.133 |
| d_aesth_1 ~ aesth_t1 | -0.081 | -0.184 | 0.021 | -0.214 | -1.550 | 0.121 |
| d_aesth_1 ~ sa07_14_t1 | 0.010 | -0.012 | 0.031 | 0.105 | 0.870 | 0.384 |
| sa07_14_t1 ~~ sa07_14_t1 | 2.002 | 1.823 | 2.180 | 1.000 | 21.957 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_aesth_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9407.546 | 9522.059 | 0.982 | 0.037 | 0.045 |
# parameters of interest
params_lcs_aesth_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_aesth_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("aesth_t2 ~ aesth_t1", "d_aesth_1 =~ aesth_t2", "d_aesth_1 ~ aesth_t1", # change parameters
"d_aesth_1 ~ frequ", "frequ ~~ frequ", "aesth_t1 ~ frequ", # frequency of self improvement
"d_aesth_1 ~1 ", "aesth_t1 ~1 ", "", # means
"d_aesth_1 ~~ d_aesth_1"))
kable(params_lcs_aesth_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| aesth_t2 ~ aesth_t1 | 1.000 | 1.000 | 1.000 | 0.998 | NA | NA |
| d_aesth_1 =~ aesth_t2 | 1.000 | 1.000 | 1.000 | 0.379 | NA | NA |
| d_aesth_1 ~1 | 0.292 | 0.001 | 0.582 | 2.304 | 1.969 | 0.049 |
| aesth_t1 ~1 | 2.848 | 2.725 | 2.970 | 8.538 | 45.482 | 0.000 |
| d_aesth_1 ~~ d_aesth_1 | 0.014 | -0.005 | 0.033 | 0.873 | 1.449 | 0.147 |
| aesth_t1 ~ frequ | -0.121 | -0.201 | -0.040 | -0.265 | -2.932 | 0.003 |
| d_aesth_1 ~ aesth_t1 | -0.102 | -0.205 | 0.001 | -0.269 | -1.942 | 0.052 |
| d_aesth_1 ~ frequ | -0.055 | -0.111 | 0.002 | -0.316 | -1.899 | 0.058 |
| frequ ~~ frequ | 0.539 | 0.360 | 0.718 | 1.000 | 5.894 | 0.000 |
Results summary (*sa07_$$_t1* = trait/facet specific acceptance goal):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_specif_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 23 | 7895.793 | 7983.587 | 0.939 | 0.074 | 0.056 |
# parameters of interest
params_lcs_imagi_curr_specif_hyp7 <- broom::tidy(fit_mi_lcs_imagi_curr_specif_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ sa07_15_t1", "sa07_15_t1 ~~ sa07_15_t1", "d_imagi_1 ~ sa07_15_t1", # acceptance goals
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_specif_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.073 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.436 | NA | NA |
| d_imagi_1 ~1 | 0.711 | -0.004 | 1.425 | 2.609 | 1.950 | 0.051 |
| imagi_t1 ~1 | 4.273 | 4.093 | 4.452 | 6.370 | 46.676 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.064 | -0.017 | 0.146 | 0.866 | 1.543 | 0.123 |
| d_imagi_1 ~ imagi_t1 | -0.154 | -0.324 | 0.015 | -0.380 | -1.788 | 0.074 |
| d_imagi_1 ~ sa07_15_t1 | -0.006 | -0.055 | 0.043 | -0.032 | -0.239 | 0.811 |
| sa07_15_t1 ~~ sa07_15_t1 | 2.135 | 1.956 | 2.314 | 1.000 | 23.329 | 0.000 |
Results summary (frequ = frequency of self-acceptance behavior):
# model fit
kable(broom::glance(fit_mi_lcs_imagi_curr_frequ_hyp7) %>%
select(nobs, npar, AIC, BIC, cfi, rmsea, srmr), digits = 3)| nobs | npar | AIC | BIC | cfi | rmsea | srmr |
|---|---|---|---|---|---|---|
| 336 | 30 | 9129.832 | 9244.345 | 0.952 | 0.058 | 0.056 |
# parameters of interest
params_lcs_imagi_curr_frequ_hyp7 <- broom::tidy(fit_mi_lcs_imagi_curr_frequ_hyp7, conf.int = TRUE, conf.level = 0.95) %>%
select(term, estimate, conf.low, conf.high, std.all, statistic, p.value) %>%
filter(term %in% c("imagi_t2 ~ imagi_t1", "d_imagi_1 =~ imagi_t2", "d_imagi_1 ~ imagi_t1", # change parameters
"d_imagi_1 ~ frequ", "frequ ~~ frequ", "imagi_t1 ~ frequ", # frequency of self improvement
"d_imagi_1 ~1 ", "imagi_t1 ~1 ", "", # means
"d_imagi_1 ~~ d_imagi_1"))
kable(params_lcs_imagi_curr_frequ_hyp7, digits = 3) | term | estimate | conf.low | conf.high | std.all | statistic | p.value |
|---|---|---|---|---|---|---|
| imagi_t2 ~ imagi_t1 | 1.000 | 1.000 | 1.000 | 1.067 | NA | NA |
| d_imagi_1 =~ imagi_t2 | 1.000 | 1.000 | 1.000 | 0.427 | NA | NA |
| d_imagi_1 ~1 | 0.661 | 0.068 | 1.254 | 2.423 | 2.185 | 0.029 |
| imagi_t1 ~1 | 3.641 | 3.536 | 3.745 | 5.343 | 68.382 | 0.000 |
| d_imagi_1 ~~ d_imagi_1 | 0.065 | -0.018 | 0.147 | 0.869 | 1.538 | 0.124 |
| imagi_t1 ~ frequ | 0.122 | -0.024 | 0.268 | 0.131 | 1.635 | 0.102 |
| d_imagi_1 ~ imagi_t1 | -0.145 | -0.301 | 0.010 | -0.363 | -1.833 | 0.067 |
| d_imagi_1 ~ frequ | 0.033 | -0.084 | 0.150 | 0.089 | 0.554 | 0.579 |
| frequ ~~ frequ | 0.537 | 0.357 | 0.717 | 1.000 | 5.841 | 0.000 |
Collect all relevant effects:
# create df for table:
# main effects (change score)
df_table_hyp7_main <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~1 "),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~1 "),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~1 "),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~1 ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_main = estimate, std_main = std.all, p_main = p.value)
# moderation effects
df_table_hyp7_mod <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_mod = estimate, std_mod = std.all, p_mod = p.value)
# combine
df_table_hyp7 <- df_table_hyp7_main %>% left_join(df_table_hyp7_mod)Results summary across the Big Five traits: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7[1:10, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| extraversion | goals | 0.674 | 2.594 | 0.027 | -0.022 | -0.073 | 0.511 |
| extraversion | frequency | 0.791 | 3.078 | 0.013 | 0.045 | 0.129 | 0.259 |
| agreeableness | goals | 0.789 | 2.857 | 0.001 | -0.022 | -0.076 | 0.419 |
| agreeableness | frequency | 0.860 | 3.146 | 0.001 | 0.062 | 0.168 | 0.085 |
| conscientiousness | goals | 1.296 | 4.110 | 0.000 | -0.043 | -0.145 | 0.057 |
| conscientiousness | frequency | 1.369 | 4.335 | 0.000 | 0.084 | 0.195 | 0.010 |
| neuroticism | goals | 0.475 | 1.357 | 0.000 | 0.000 | -0.001 | 0.993 |
| neuroticism | frequency | 0.481 | 1.368 | 0.000 | -0.015 | -0.032 | 0.711 |
| openness | goals | 1.229 | 5.729 | 0.000 | -0.025 | -0.092 | 0.219 |
| openness | frequency | 1.175 | 5.437 | 0.000 | 0.027 | 0.093 | 0.257 |
Results summary across the Big Five facets: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7[11:40, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| sociability | goals | 0.060 | 0.314 | 0.913 | -0.049 | -0.344 | 0.044 |
| sociability | frequency | 0.001 | 0.003 | 0.999 | -0.021 | -0.080 | 0.678 |
| assertiveness | goals | 0.004 | 0.023 | 0.994 | 0.024 | 0.196 | 0.207 |
| assertiveness | frequency | 0.015 | 0.089 | 0.977 | -0.009 | -0.039 | 0.839 |
| energy | goals | 0.668 | 4.147 | 0.001 | -0.002 | -0.016 | 0.839 |
| energy | frequency | 0.797 | 4.548 | 0.000 | -0.060 | -0.252 | 0.066 |
| compassion | goals | 0.876 | 2.470 | 0.075 | -0.009 | -0.035 | 0.774 |
| compassion | frequency | 0.869 | 2.351 | 0.059 | 0.028 | 0.055 | 0.665 |
| respectfulness | goals | 0.380 | 2.533 | 0.369 | -0.007 | -0.066 | 0.680 |
| respectfulness | frequency | 0.429 | 2.916 | 0.313 | 0.043 | 0.213 | 0.221 |
| trust | goals | 0.475 | 1.731 | 0.002 | -0.011 | -0.057 | 0.595 |
| trust | frequency | 0.508 | 1.857 | 0.001 | -0.097 | -0.262 | 0.042 |
| organization | goals | 0.219 | 0.829 | 0.234 | 0.022 | 0.120 | 0.278 |
| organization | frequency | 0.296 | 1.130 | 0.154 | -0.023 | -0.065 | 0.641 |
| productiveness | goals | 0.285 | 1.193 | 0.032 | 0.026 | 0.150 | 0.091 |
| productiveness | frequency | 0.397 | 1.656 | 0.002 | -0.088 | -0.267 | 0.011 |
| responsibility | goals | 0.669 | 2.523 | 0.152 | -0.024 | -0.129 | 0.178 |
| responsibility | frequency | 0.685 | 2.524 | 0.138 | 0.080 | 0.217 | 0.046 |
| anxiety | goals | 1.543 | 6.100 | 0.004 | -0.005 | -0.028 | 0.762 |
| anxiety | frequency | 1.460 | 5.662 | 0.008 | -0.034 | -0.099 | 0.418 |
| depression | goals | 1.393 | 5.262 | 0.000 | -0.004 | -0.022 | 0.792 |
| depression | frequency | 1.382 | 5.108 | 0.000 | 0.003 | 0.008 | 0.935 |
| volatility | goals | 0.330 | 1.298 | 0.062 | -0.030 | -0.152 | 0.146 |
| volatility | frequency | 0.293 | 1.140 | 0.105 | -0.052 | -0.148 | 0.318 |
| curiosity | goals | 0.885 | 4.194 | 0.029 | 0.006 | 0.035 | 0.757 |
| curiosity | frequency | 0.914 | 4.446 | 0.012 | 0.029 | 0.104 | 0.414 |
| aesthetic | goals | 0.468 | 2.754 | 0.046 | 0.004 | 0.033 | 0.679 |
| aesthetic | frequency | 0.542 | 3.330 | 0.024 | -0.038 | -0.170 | 0.194 |
| imagination | goals | 1.207 | 3.643 | 0.008 | -0.027 | -0.119 | 0.184 |
| imagination | frequency | 1.054 | 3.204 | 0.019 | 0.000 | -0.001 | 0.996 |
Prepare data frame for plotting:
df_table_hyp7_plot <- bind_rows(
#traits
params_lcs_extra_ideal_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_ideal_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_ideal_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_ideal_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_ideal_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_ideal_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_ideal_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_ideal_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_ideal_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_ideal_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_ideal_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_ideal_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_ideal_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_ideal_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_ideal_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_ideal_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_ideal_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_ideal_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_ideal_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_ideal_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_ideal_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_ideal_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_ideal_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_ideal_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_ideal_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_ideal_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_ideal_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_ideal_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_ideal_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_ideal_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_ideal_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_ideal_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_ideal_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_ideal_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_ideal_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_ideal_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_ideal_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_ideal_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_ideal_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_ideal_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp7_plot <- df_table_hyp7_plot %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("ideal", levels = c("ideal"), labels = c("ideal"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp7_plot, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Collect all relevant effects:
# create df for table:
# main effects (change score)
df_table_hyp7_curr_main <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp7 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_extra_curr_frequ_hyp7 %>% filter(term=="d_extra_1 ~1 "),
params_lcs_agree_curr_specif_hyp7 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_agree_curr_frequ_hyp7 %>% filter(term=="d_agree_1 ~1 "),
params_lcs_consc_curr_specif_hyp7 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_consc_curr_frequ_hyp7 %>% filter(term=="d_consc_1 ~1 "),
params_lcs_neuro_curr_specif_hyp7 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_neuro_curr_frequ_hyp7 %>% filter(term=="d_neuro_1 ~1 "),
params_lcs_openn_curr_specif_hyp7 %>% filter(term=="d_openn_1 ~1 "),
params_lcs_openn_curr_frequ_hyp7 %>% filter(term=="d_openn_1 ~1 "),
#facets
params_lcs_socia_curr_specif_hyp7 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_socia_curr_frequ_hyp7 %>% filter(term=="d_socia_1 ~1 "),
params_lcs_asser_curr_specif_hyp7 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_asser_curr_frequ_hyp7 %>% filter(term=="d_asser_1 ~1 "),
params_lcs_energ_curr_specif_hyp7 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_energ_curr_frequ_hyp7 %>% filter(term=="d_energ_1 ~1 "),
params_lcs_compa_curr_specif_hyp7 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_compa_curr_frequ_hyp7 %>% filter(term=="d_compa_1 ~1 "),
params_lcs_respe_curr_specif_hyp7 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_respe_curr_frequ_hyp7 %>% filter(term=="d_respe_1 ~1 "),
params_lcs_trust_curr_specif_hyp7 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_trust_curr_frequ_hyp7 %>% filter(term=="d_trust_1 ~1 "),
params_lcs_organ_curr_specif_hyp7 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_organ_curr_frequ_hyp7 %>% filter(term=="d_organ_1 ~1 "),
params_lcs_produ_curr_specif_hyp7 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_produ_curr_frequ_hyp7 %>% filter(term=="d_produ_1 ~1 "),
params_lcs_respo_curr_specif_hyp7 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_respo_curr_frequ_hyp7 %>% filter(term=="d_respo_1 ~1 "),
params_lcs_anxie_curr_specif_hyp7 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_anxie_curr_frequ_hyp7 %>% filter(term=="d_anxie_1 ~1 "),
params_lcs_depre_curr_specif_hyp7 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_depre_curr_frequ_hyp7 %>% filter(term=="d_depre_1 ~1 "),
params_lcs_volat_curr_specif_hyp7 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_volat_curr_frequ_hyp7 %>% filter(term=="d_volat_1 ~1 "),
params_lcs_curio_curr_specif_hyp7 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_curio_curr_frequ_hyp7 %>% filter(term=="d_curio_1 ~1 "),
params_lcs_aesth_curr_specif_hyp7 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_aesth_curr_frequ_hyp7 %>% filter(term=="d_aesth_1 ~1 "),
params_lcs_imagi_curr_specif_hyp7 %>% filter(term=="d_imagi_1 ~1 "),
params_lcs_imagi_curr_frequ_hyp7 %>% filter(term=="d_imagi_1 ~1 ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_main = estimate, std_main = std.all, p_main = p.value)
# moderation effects
df_table_hyp7_curr_mod <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_curr_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_curr_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_curr_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_curr_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_curr_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_curr_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_curr_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_curr_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_curr_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_curr_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_curr_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_curr_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_curr_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_curr_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_curr_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, est_mod = estimate, std_mod = std.all, p_mod = p.value)
# combine
df_table_hyp7_curr <- df_table_hyp7_curr_main %>% left_join(df_table_hyp7_curr_mod)Results summary across the Big Five traits: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7_curr[1:10, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| extraversion | goals | 0.510 | 1.902 | 0.002 | -0.015 | -0.055 | 0.715 |
| extraversion | frequency | 0.470 | 1.802 | 0.000 | 0.046 | 0.130 | 0.211 |
| agreeableness | goals | 0.550 | 2.733 | 0.024 | -0.051 | -0.247 | 0.060 |
| agreeableness | frequency | 0.421 | 2.051 | 0.059 | 0.036 | 0.128 | 0.214 |
| conscientiousness | goals | 0.578 | 1.660 | 0.002 | -0.029 | -0.092 | 0.426 |
| conscientiousness | frequency | 0.499 | 1.434 | 0.000 | 0.043 | 0.091 | 0.256 |
| neuroticism | goals | 0.244 | 0.655 | 0.066 | 0.046 | 0.125 | 0.202 |
| neuroticism | frequency | 0.117 | 0.312 | 0.267 | -0.101 | -0.201 | 0.029 |
| openness | goals | 0.435 | 1.838 | 0.010 | -0.001 | -0.002 | 0.987 |
| openness | frequency | 0.484 | 2.028 | 0.000 | 0.071 | 0.218 | 0.086 |
Results summary across the Big Five facets: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7_curr[11:40, ], digits = 3)| trait | moderator | est_main | std_main | p_main | est_mod | std_mod | p_mod |
|---|---|---|---|---|---|---|---|
| sociability | goals | 0.651 | 1.478 | 0.001 | -0.002 | -0.006 | 0.950 |
| sociability | frequency | 0.636 | 1.460 | 0.000 | -0.020 | -0.034 | 0.748 |
| assertiveness | goals | 0.199 | 0.728 | 0.249 | 0.017 | 0.081 | 0.470 |
| assertiveness | frequency | 0.280 | 1.076 | 0.023 | 0.074 | 0.208 | 0.122 |
| energy | goals | 0.306 | 1.703 | 0.021 | -0.001 | -0.011 | 0.921 |
| energy | frequency | 0.324 | 1.757 | 0.015 | -0.036 | -0.143 | 0.183 |
| compassion | goals | 0.531 | 1.396 | 0.228 | -0.038 | -0.141 | 0.262 |
| compassion | frequency | 0.322 | 0.820 | 0.380 | -0.012 | -0.022 | 0.856 |
| respectfulness | goals | 1.468 | 4.458 | 0.000 | -0.094 | -0.376 | 0.002 |
| respectfulness | frequency | 0.919 | 2.776 | 0.019 | 0.052 | 0.115 | 0.230 |
| trust | goals | 0.199 | 0.928 | 0.213 | -0.056 | -0.360 | 0.042 |
| trust | frequency | 0.150 | 0.706 | 0.352 | -0.052 | -0.179 | 0.322 |
| organization | goals | 0.297 | 0.628 | 0.025 | 0.055 | 0.167 | 0.109 |
| organization | frequency | 0.342 | 0.736 | 0.012 | -0.050 | -0.079 | 0.423 |
| productiveness | goals | 0.279 | 0.708 | 0.102 | 0.006 | 0.022 | 0.853 |
| productiveness | frequency | 0.320 | 0.814 | 0.061 | -0.097 | -0.181 | 0.095 |
| responsibility | goals | 0.551 | 3.055 | 0.039 | -0.001 | -0.010 | 0.936 |
| responsibility | frequency | 0.528 | 2.878 | 0.004 | -0.005 | -0.020 | 0.844 |
| anxiety | goals | 0.607 | 1.480 | 0.004 | -0.037 | -0.132 | 0.227 |
| anxiety | frequency | 0.346 | 0.834 | 0.010 | 0.160 | 0.290 | 0.020 |
| depression | goals | 0.575 | 2.010 | 0.000 | -0.013 | -0.066 | 0.432 |
| depression | frequency | 0.437 | 1.538 | 0.000 | 0.085 | 0.222 | 0.030 |
| volatility | goals | 0.330 | 0.815 | 0.012 | -0.016 | -0.050 | 0.668 |
| volatility | frequency | 0.313 | 0.771 | 0.019 | 0.018 | 0.032 | 0.780 |
| curiosity | goals | 0.073 | NA | 0.774 | 0.040 | NA | 0.028 |
| curiosity | frequency | 0.315 | NA | 0.143 | 0.053 | NA | 0.147 |
| aesthetic | goals | 0.206 | 1.594 | 0.132 | 0.010 | 0.105 | 0.384 |
| aesthetic | frequency | 0.292 | 2.304 | 0.049 | -0.055 | -0.316 | 0.058 |
| imagination | goals | 0.711 | 2.609 | 0.051 | -0.006 | -0.032 | 0.811 |
| imagination | frequency | 0.661 | 2.423 | 0.029 | 0.033 | 0.089 | 0.579 |
Prepare data frame for plotting:
df_table_hyp7_plot_curr <- bind_rows(
#traits
params_lcs_extra_curr_specif_hyp7 %>% filter(term=="d_extra_1 ~ goals"),
params_lcs_extra_curr_frequ_hyp7 %>% filter(term=="d_extra_1 ~ frequ"),
params_lcs_agree_curr_specif_hyp7 %>% filter(term=="d_agree_1 ~ goals"),
params_lcs_agree_curr_frequ_hyp7 %>% filter(term=="d_agree_1 ~ frequ"),
params_lcs_consc_curr_specif_hyp7 %>% filter(term=="d_consc_1 ~ goals"),
params_lcs_consc_curr_frequ_hyp7 %>% filter(term=="d_consc_1 ~ frequ"),
params_lcs_neuro_curr_specif_hyp7 %>% filter(term=="d_neuro_1 ~ goals"),
params_lcs_neuro_curr_frequ_hyp7 %>% filter(term=="d_neuro_1 ~ frequ"),
params_lcs_openn_curr_specif_hyp7 %>% filter(term=="d_openn_1 ~ goals"),
params_lcs_openn_curr_frequ_hyp7 %>% filter(term=="d_openn_1 ~ frequ"),
#facets
params_lcs_socia_curr_specif_hyp7 %>% filter(term=="d_socia_1 ~ sa07_01_t1"),
params_lcs_socia_curr_frequ_hyp7 %>% filter(term=="d_socia_1 ~ frequ"),
params_lcs_asser_curr_specif_hyp7 %>% filter(term=="d_asser_1 ~ sa07_02_t1"),
params_lcs_asser_curr_frequ_hyp7 %>% filter(term=="d_asser_1 ~ frequ"),
params_lcs_energ_curr_specif_hyp7 %>% filter(term=="d_energ_1 ~ sa07_03_t1"),
params_lcs_energ_curr_frequ_hyp7 %>% filter(term=="d_energ_1 ~ frequ"),
params_lcs_compa_curr_specif_hyp7 %>% filter(term=="d_compa_1 ~ sa07_04_t1"),
params_lcs_compa_curr_frequ_hyp7 %>% filter(term=="d_compa_1 ~ frequ"),
params_lcs_respe_curr_specif_hyp7 %>% filter(term=="d_respe_1 ~ sa07_05_t1"),
params_lcs_respe_curr_frequ_hyp7 %>% filter(term=="d_respe_1 ~ frequ"),
params_lcs_trust_curr_specif_hyp7 %>% filter(term=="d_trust_1 ~ sa07_06_t1"),
params_lcs_trust_curr_frequ_hyp7 %>% filter(term=="d_trust_1 ~ frequ"),
params_lcs_organ_curr_specif_hyp7 %>% filter(term=="d_organ_1 ~ sa07_07_t1"),
params_lcs_organ_curr_frequ_hyp7 %>% filter(term=="d_organ_1 ~ frequ"),
params_lcs_produ_curr_specif_hyp7 %>% filter(term=="d_produ_1 ~ sa07_08_t1"),
params_lcs_produ_curr_frequ_hyp7 %>% filter(term=="d_produ_1 ~ frequ"),
params_lcs_respo_curr_specif_hyp7 %>% filter(term=="d_respo_1 ~ sa07_09_t1"),
params_lcs_respo_curr_frequ_hyp7 %>% filter(term=="d_respo_1 ~ frequ"),
params_lcs_anxie_curr_specif_hyp7 %>% filter(term=="d_anxie_1 ~ sa07_10_t1"),
params_lcs_anxie_curr_frequ_hyp7 %>% filter(term=="d_anxie_1 ~ frequ"),
params_lcs_depre_curr_specif_hyp7 %>% filter(term=="d_depre_1 ~ sa07_11_t1"),
params_lcs_depre_curr_frequ_hyp7 %>% filter(term=="d_depre_1 ~ frequ"),
params_lcs_volat_curr_specif_hyp7 %>% filter(term=="d_volat_1 ~ sa07_12_t1"),
params_lcs_volat_curr_frequ_hyp7 %>% filter(term=="d_volat_1 ~ frequ"),
params_lcs_curio_curr_specif_hyp7 %>% filter(term=="d_curio_1 ~ sa07_13_t1"),
params_lcs_curio_curr_frequ_hyp7 %>% filter(term=="d_curio_1 ~ frequ"),
params_lcs_aesth_curr_specif_hyp7 %>% filter(term=="d_aesth_1 ~ sa07_14_t1"),
params_lcs_aesth_curr_frequ_hyp7 %>% filter(term=="d_aesth_1 ~ frequ"),
params_lcs_imagi_curr_specif_hyp7 %>% filter(term=="d_imagi_1 ~ sa07_15_t1"),
params_lcs_imagi_curr_frequ_hyp7 %>% filter(term=="d_imagi_1 ~ frequ")
) %>%
mutate(trait = rep(names(b5_vars), each=2),
moderator = rep(c("goals", "frequency"), 20)) %>%
select(trait, moderator, estimate, conf.low, conf.high, std.all, statistic, p.value)
df_table_hyp7_plot_curr <- df_table_hyp7_plot_curr %>%
mutate(include_0 = ifelse(conf.low < 0 & conf.high > 0, "n.s.", "*")) %>%
mutate(ref = factor("current", levels = c("current"), labels = c("current"))) %>%
mutate(moderator = factor(moderator, levels = c("goals", "frequency"), labels = c("goals", "frequency"))) %>%
mutate(trait = factor(trait, levels = names(b5_vars), labels = names(b5_vars)))Plotting the effect size across all analyses:
ggplot(df_table_hyp7_plot_curr, aes(x = fct_rev(trait), y = estimate, color = include_0)) +
geom_hline(yintercept=0, linetype = 3) +
geom_point(size=3, position=position_dodge(0.4)) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, color = include_0), width=.2, position=position_dodge(0.4)) +
scale_color_manual(values = c("#000000","#A9A9A9")) +
facet_wrap( ~ ref + moderator, ncol = 2) +
theme_bw() +
scale_shape_manual(values=c(18)) +
ylab("Effect Estimates (95% CI)") +
xlab("") +
theme(legend.title=element_blank()) +
theme(legend.text=element_text(size=12)) +
coord_flip() +
theme(strip.text.x = element_text(size = 12)) +
theme(axis.text.x=element_text(size=10, angle = 45, hjust = 1), axis.text.y=element_text(size=12)) +
guides(color="none")
Change in psychological well-being indicators as well as the difference between real- and ideal-self will differ across groups at follow-up. Based on exploratory results from Study 1, we expect that the self-acceptance intervention leads to more pronounced well-being gains and less pronounced differences between current- and ideal-self personality compared to the self-improvement intervention.
Life satisfaction: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_swls_group_config <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2a", "lamb2b")*sw06_02_t1 + c("lamb3a", "lamb3b")*sw06_03_t1 + c("lamb4a", "lamb4b")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2a", "lamb2b")*sw06_02_t2 + c("lamb3a", "lamb3b")*sw06_03_t2 + c("lamb4a", "lamb4b")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_config <- sem(mi_lcs_swls_group_config, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_swls_group_weak <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_weak <- sem(mi_lcs_swls_group_weak, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_swls_group_strong <- '
swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1
swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadings
swls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1
d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2
swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0
swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0
d_swls_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
swls_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of swls_t1
d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores
swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1
d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parameter
sw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2
sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2
sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2
sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2
sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1
sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1
sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1
sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1
sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2
sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2
sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2
sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2
sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
sw06_02_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
sw06_03_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
sw06_04_t1 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T1
sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
sw06_02_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
sw06_03_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
sw06_04_t2 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T2
'
lcs_swls_group_strong <- sem(mi_lcs_swls_group_strong, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Life satisfaction: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_swls_group_config, lcs_swls_group_weak, lcs_swls_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_swls_group_config 50 13077 13248 125.00
lcs_swls_group_weak 53 13073 13231 127.59 3.1604 3 0.36755
lcs_swls_group_strong 58 13073 13208 137.46 9.9114 5 0.07779 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# show model with varying latent change parameters
# -> key parameter is "d_swls_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_swls_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_swls_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_swls_1 ~1 | d_int_a | 0.559 | 0.952 | 4.360 | 0 |
| d_swls_1 ~1 | d_int_b | 1.054 | 1.477 | 6.972 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_swls_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_swls_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_swls_1 ~1 | d_int | 0.799 | 1.281 | 7.851 | 0 |
| d_swls_1 ~1 | d_int | 0.799 | 1.173 | 7.851 | 0 |
# whole model (weak invariance)
summary(lcs_swls_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 59 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 58
Number of equality constraints 23
Number of observations per group: Used Total
Self-Acceptance 336 336
Self-Improvement 337 338
Number of missing patterns per group:
Self-Acceptance 2
Self-Improvement 2
Model Test User Model:
Standard Scaled
Test Statistic 127.587 114.982
Degrees of freedom 53 53
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.110
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 68.718 61.929
Self-Improvement 58.869 53.053
Model Test Baseline Model:
Test statistic 3183.172 2343.602
Degrees of freedom 56 56
P-value 0.000 0.000
Scaling correction factor 1.358
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.976 0.973
Tucker-Lewis Index (TLI) 0.975 0.971
Robust Comparative Fit Index (CFI) 0.978
Robust Tucker-Lewis Index (TLI) 0.977
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -6501.579 -6501.579
Scaling correction factor 0.703
for the MLR correction
Loglikelihood unrestricted model (H1) -6437.786 -6437.786
Scaling correction factor 1.132
for the MLR correction
Akaike (AIC) 13073.158 13073.158
Bayesian (BIC) 13231.070 13231.070
Sample-size adjusted Bayesian (SABIC) 13119.942 13119.942
Root Mean Square Error of Approximation:
RMSEA 0.065 0.059
90 Percent confidence interval - lower 0.050 0.045
90 Percent confidence interval - upper 0.079 0.073
P-value H_0: RMSEA <= 0.050 0.046 0.140
P-value H_0: RMSEA >= 0.080 0.040 0.006
Robust RMSEA 0.065
90 Percent confidence interval - lower 0.048
90 Percent confidence interval - upper 0.082
P-value H_0: Robust RMSEA <= 0.050 0.073
P-value H_0: Robust RMSEA >= 0.080 0.068
Standardized Root Mean Square Residual:
SRMR 0.035 0.035
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t1 =~
s06_01_ 1.000 0.993 0.839
s06_02_ (lmb2) 0.885 0.029 30.812 0.000 0.879 0.775
s06_03_ (lmb3) 1.038 0.031 33.314 0.000 1.031 0.858
s06_04_ (lmb4) 0.909 0.031 29.375 0.000 0.903 0.721
swls_t2 =~
s06_01_ 1.000 1.052 0.853
s06_02_ (lmb2) 0.885 0.029 30.812 0.000 0.931 0.793
s06_03_ (lmb3) 1.038 0.031 33.314 0.000 1.092 0.870
s06_04_ (lmb4) 0.909 0.031 29.375 0.000 0.956 0.741
d_swls_1 =~
swls_t2 1.000 0.559 0.559
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t2 ~
swls_t1 1.000 0.944 0.944
d_swls_1 ~
swls_t1 (fb_a) -0.114 0.040 -2.881 0.004 -0.193 -0.193
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.sw06_01_t1 ~~
.s06_01_ (cov1) 0.050 0.044 1.136 0.256 0.050 0.121
.sw06_02_t1 ~~
.s06_02_ (cov2) 0.210 0.043 4.924 0.000 0.210 0.408
.sw06_03_t1 ~~
.s06_03_ (cov3) 0.034 0.040 0.859 0.390 0.034 0.089
.sw06_04_t1 ~~
.s06_04_ (cov4) 0.289 0.064 4.514 0.000 0.289 0.384
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_n_) 0.559 0.128 4.360 0.000 0.952 0.952
swls_t1 (wb__) 2.742 0.062 43.993 0.000 2.761 2.761
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2a) 0.499 0.098 5.078 0.000 0.499 0.440
.s06_03_ (m3a) 0.025 0.102 0.250 0.803 0.025 0.021
.s06_04_ (m4a) 0.420 0.103 4.096 0.000 0.420 0.336
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2a) 0.499 0.098 5.078 0.000 0.499 0.425
.s06_03_ (m3a) 0.025 0.102 0.250 0.803 0.025 0.020
.s06_04_ (m4a) 0.420 0.103 4.096 0.000 0.420 0.326
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_v_) 0.332 0.053 6.308 0.000 0.963 0.963
swls_t1 (wb__) 0.986 0.075 13.185 0.000 1.000 1.000
.s06_01_ (res1) 0.414 0.044 9.393 0.000 0.414 0.296
.s06_02_ (res2) 0.513 0.040 12.737 0.000 0.513 0.399
.s06_03_ (res3) 0.381 0.038 10.087 0.000 0.381 0.264
.s06_04_ (res4) 0.752 0.061 12.251 0.000 0.752 0.480
.s06_01_ (res1) 0.414 0.044 9.393 0.000 0.414 0.272
.s06_02_ (res2) 0.513 0.040 12.737 0.000 0.513 0.372
.s06_03_ (res3) 0.381 0.038 10.087 0.000 0.381 0.242
.s06_04_ (res4) 0.752 0.061 12.251 0.000 0.752 0.451
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t1 =~
s06_01_ 1.000 0.982 0.815
s06_02_ (lmb2) 0.885 0.029 30.812 0.000 0.869 0.739
s06_03_ (lmb3) 1.038 0.031 33.314 0.000 1.019 0.851
s06_04_ (lmb4) 0.909 0.031 29.375 0.000 0.892 0.706
swls_t2 =~
s06_01_ 1.000 0.958 0.809
s06_02_ (lmb2) 0.885 0.029 30.812 0.000 0.848 0.731
s06_03_ (lmb3) 1.038 0.031 33.314 0.000 0.994 0.845
s06_04_ (lmb4) 0.909 0.031 29.375 0.000 0.871 0.697
d_swls_1 =~
swls_t2 1.000 0.745 0.745
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
swls_t2 ~
swls_t1 1.000 1.025 1.025
d_swls_1 ~
swls_t1 (fb_b) -0.288 0.053 -5.463 0.000 -0.397 -0.397
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.sw06_01_t1 ~~
.s06_01_ (cv1b) 0.132 0.044 2.974 0.003 0.132 0.272
.sw06_02_t1 ~~
.s06_02_ (cv2b) 0.297 0.057 5.231 0.000 0.297 0.474
.sw06_03_t1 ~~
.s06_03_ (cv3b) 0.068 0.043 1.582 0.114 0.068 0.173
.sw06_04_t1 ~~
.s06_04_ (cv4b) 0.320 0.066 4.823 0.000 0.320 0.400
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_n_) 1.054 0.151 6.972 0.000 1.477 1.477
swls_t1 (wb__) 2.629 0.063 41.512 0.000 2.679 2.679
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2b) 0.533 0.094 5.691 0.000 0.533 0.453
.s06_03_ (m3b) 0.106 0.098 1.088 0.277 0.106 0.089
.s06_04_ (m4b) 0.429 0.102 4.202 0.000 0.429 0.339
.s06_01_ 0.000 0.000 0.000
.s06_02_ (m2b) 0.533 0.094 5.691 0.000 0.533 0.459
.s06_03_ (m3b) 0.106 0.098 1.088 0.277 0.106 0.090
.s06_04_ (m4b) 0.429 0.102 4.202 0.000 0.429 0.344
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.swls_t2 0.000 0.000 0.000
.d_swl_1 (d_v_) 0.429 0.060 7.101 0.000 0.843 0.843
swls_t1 (wb__) 0.963 0.075 12.868 0.000 1.000 1.000
.s06_01_ (rs1b) 0.485 0.050 9.762 0.000 0.485 0.335
.s06_02_ (rs2b) 0.627 0.056 11.267 0.000 0.627 0.454
.s06_03_ (rs3b) 0.395 0.039 10.203 0.000 0.395 0.276
.s06_04_ (rs4b) 0.800 0.063 12.669 0.000 0.800 0.501
.s06_01_ (rs1b) 0.485 0.050 9.762 0.000 0.485 0.346
.s06_02_ (rs2b) 0.627 0.056 11.267 0.000 0.627 0.466
.s06_03_ (rs3b) 0.395 0.039 10.203 0.000 0.395 0.286
.s06_04_ (rs4b) 0.800 0.063 12.669 0.000 0.800 0.514Meaning in life: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_meaning_group_config <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2a", "lamb2b")*meaning_par2_t1 + c("lamb3a", "lamb3b")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2a", "lamb2b")*meaning_par2_t2 + c("lamb3a", "lamb3b")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_config <- sem(mi_lcs_meaning_group_config, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_meaning_group_weak <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_weak <- sem(mi_lcs_meaning_group_weak, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_meaning_group_strong <- '
meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1
meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadings
meaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1
d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2
meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0
meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0
d_meaning_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
meaning_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of meaning_t1
d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores
meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1
d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parameter
meaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1
meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1
meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1
meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2
meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2
meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2
meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
meaning_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
meaning_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
meaning_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
meaning_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_meaning_group_strong <- sem(mi_lcs_meaning_group_strong, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_meaning_group_config, lcs_meaning_group_weak, lcs_meaning_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_meaning_group_config 24 9552.9 9688.3 49.776
lcs_meaning_group_weak 26 9550.4 9676.8 51.287 1.5699 2 0.4561
lcs_meaning_group_strong 30 9544.3 9652.5 53.131 1.7531 4 0.7810# show model with varying latent change parameters
# -> key parameter is "d_meaning_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_meaning_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_meaning_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_meaning_1 ~1 | d_int_a | 0.931 | 1.522 | 6.622 | 0 |
| d_meaning_1 ~1 | d_int_b | 1.129 | 1.564 | 6.169 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_meaning_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_meaning_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_meaning_1 ~1 | d_int | 1.01 | 1.620 | 8.933 | 0 |
| d_meaning_1 ~1 | d_int | 1.01 | 1.426 | 8.933 | 0 |
# whole model (weak invariance)
summary(lcs_meaning_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 63 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 336 336
Self-Improvement 337 338
Number of missing patterns per group:
Self-Acceptance 2
Self-Improvement 2
Model Test User Model:
Standard Scaled
Test Statistic 51.287 46.282
Degrees of freedom 26 26
P-value (Chi-square) 0.002 0.008
Scaling correction factor 1.108
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 29.436 26.563
Self-Improvement 21.851 19.719
Model Test Baseline Model:
Test statistic 2949.985 2233.784
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.321
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.991 0.991
Tucker-Lewis Index (TLI) 0.990 0.989
Robust Comparative Fit Index (CFI) 0.992
Robust Tucker-Lewis Index (TLI) 0.990
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4747.211 -4747.211
Scaling correction factor 0.749
for the MLR correction
Loglikelihood unrestricted model (H1) -4721.568 -4721.568
Scaling correction factor 1.144
for the MLR correction
Akaike (AIC) 9550.422 9550.422
Bayesian (BIC) 9676.751 9676.751
Sample-size adjusted Bayesian (SABIC) 9587.848 9587.848
Root Mean Square Error of Approximation:
RMSEA 0.054 0.048
90 Percent confidence interval - lower 0.032 0.026
90 Percent confidence interval - upper 0.075 0.069
P-value H_0: RMSEA <= 0.050 0.361 0.529
P-value H_0: RMSEA >= 0.080 0.021 0.005
Robust RMSEA 0.055
90 Percent confidence interval - lower 0.028
90 Percent confidence interval - upper 0.080
P-value H_0: Robust RMSEA <= 0.050 0.351
P-value H_0: Robust RMSEA >= 0.080 0.047
Standardized Root Mean Square Residual:
SRMR 0.033 0.033
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t1 =~
mnn_1_1 1.000 1.002 0.814
mnn_2_1 (lmb2) 1.201 0.035 34.437 0.000 1.204 0.929
mnn_3_1 (lmb3) 1.168 0.035 33.501 0.000 1.170 0.885
meaning_t2 =~
mnn_1_2 1.000 0.958 0.802
mnn_2_2 (lmb2) 1.201 0.035 34.437 0.000 1.151 0.923
mnn_3_2 (lmb3) 1.168 0.035 33.501 0.000 1.119 0.877
d_meaning_1 =~
mnng_t2 1.000 0.638 0.638
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t2 ~
mnng_t1 1.000 1.045 1.045
d_meaning_1 ~
mnng_t1 (fb_a) -0.229 0.040 -5.730 0.000 -0.375 -0.375
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.meaning_par1_t1 ~~
.mnn_1_2 (cov1) 0.207 0.043 4.842 0.000 0.207 0.406
.meaning_par2_t1 ~~
.mnn_2_2 (cov2) 0.027 0.033 0.830 0.407 0.027 0.118
.meaning_par3_t1 ~~
.mnn_3_2 (cov3) 0.153 0.038 4.081 0.000 0.153 0.406
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_n_) 0.931 0.141 6.622 0.000 1.522 1.522
mnng_t1 (wb__) 3.210 0.064 50.291 0.000 3.204 3.204
.mnn_1_1 0.000 0.000 0.000
.mnn_2_1 (m2a) -0.048 0.121 -0.393 0.695 -0.048 -0.037
.mnn_3_1 (m3a) -0.116 0.118 -0.985 0.324 -0.116 -0.088
.mnn_1_2 0.000 0.000 0.000
.mnn_2_2 (m2a) -0.048 0.121 -0.393 0.695 -0.048 -0.038
.mnn_3_2 (m3a) -0.116 0.118 -0.985 0.324 -0.116 -0.091
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_v_) 0.322 0.057 5.676 0.000 0.859 0.859
mnng_t1 (wb__) 1.004 0.096 10.450 0.000 1.000 1.000
.mnn_1_1 (res1) 0.510 0.040 12.813 0.000 0.510 0.337
.mnn_2_1 (res2) 0.230 0.035 6.582 0.000 0.230 0.137
.mnn_3_1 (res3) 0.377 0.038 9.898 0.000 0.377 0.216
.mnn_1_2 (res1) 0.510 0.040 12.813 0.000 0.510 0.357
.mnn_2_2 (res2) 0.230 0.035 6.582 0.000 0.230 0.148
.mnn_3_2 (res3) 0.377 0.038 9.898 0.000 0.377 0.232
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t1 =~
mnn_1_1 1.000 0.951 0.786
mnn_2_1 (lmb2) 1.201 0.035 34.437 0.000 1.142 0.902
mnn_3_1 (lmb3) 1.168 0.035 33.501 0.000 1.110 0.854
meaning_t2 =~
mnn_1_2 1.000 0.958 0.788
mnn_2_2 (lmb2) 1.201 0.035 34.437 0.000 1.151 0.903
mnn_3_2 (lmb3) 1.168 0.035 33.501 0.000 1.119 0.856
d_meaning_1 =~
mnng_t2 1.000 0.753 0.753
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
meaning_t2 ~
mnng_t1 1.000 0.992 0.992
d_meaning_1 ~
mnng_t1 (fb_b) -0.280 0.052 -5.401 0.000 -0.369 -0.369
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.meaning_par1_t1 ~~
.mnn_1_2 (cv1b) 0.281 0.044 6.434 0.000 0.281 0.502
.meaning_par2_t1 ~~
.mnn_2_2 (cv2b) 0.076 0.040 1.920 0.055 0.076 0.253
.meaning_par3_t1 ~~
.mnn_3_2 (cv3b) 0.137 0.048 2.881 0.004 0.137 0.300
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_n_) 1.129 0.183 6.169 0.000 1.564 1.564
mnng_t1 (wb__) 3.206 0.060 53.091 0.000 3.371 3.371
.mnn_1_1 0.000 0.000 0.000
.mnn_2_1 (m2b) -0.036 0.123 -0.289 0.772 -0.036 -0.028
.mnn_3_1 (m3b) -0.061 0.122 -0.504 0.615 -0.061 -0.047
.mnn_1_2 0.000 0.000 0.000
.mnn_2_2 (m2b) -0.036 0.123 -0.289 0.772 -0.036 -0.028
.mnn_3_2 (m3b) -0.061 0.122 -0.504 0.615 -0.061 -0.047
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.mnng_t2 0.000 0.000 0.000
.d_mnn_1 (d_v_) 0.450 0.062 7.306 0.000 0.864 0.864
mnng_t1 (wb__) 0.904 0.085 10.690 0.000 1.000 1.000
.mnn_1_1 (rs1b) 0.560 0.045 12.505 0.000 0.560 0.382
.mnn_2_1 (rs2b) 0.300 0.038 7.806 0.000 0.300 0.187
.mnn_3_1 (rs3b) 0.458 0.049 9.323 0.000 0.458 0.271
.mnn_1_2 (rs1b) 0.560 0.045 12.505 0.000 0.560 0.379
.mnn_2_2 (rs2b) 0.300 0.038 7.806 0.000 0.300 0.185
.mnn_3_2 (rs3b) 0.458 0.049 9.323 0.000 0.458 0.268Self-esteem: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_selfes_group_config <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2a", "lamb2b")*selfes_par2_t1 + c("lamb3a", "lamb3b")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2a", "lamb2b")*selfes_par2_t2 + c("lamb3a", "lamb3b")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_config <- sem(mi_lcs_selfes_group_config, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_selfes_group_weak <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_weak <- sem(mi_lcs_selfes_group_weak, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_selfes_group_strong <- '
selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1
selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadings
selfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1
d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2
selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0
selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0
d_selfes_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
selfes_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of selfes_t1
d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores
selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1
d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameter
selfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1
selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1
selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1
selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2
selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2
selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2
selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
selfes_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
selfes_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
selfes_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
selfes_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_selfes_group_strong <- sem(mi_lcs_selfes_group_strong, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_selfes_group_config, lcs_selfes_group_weak, lcs_selfes_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_selfes_group_config 24 7216.5 7351.9 16.395
lcs_selfes_group_weak 26 7213.2 7339.6 17.124 0.7634 2 0.6827
lcs_selfes_group_strong 30 7212.7 7321.0 24.562 7.1744 4 0.1270# show model with varying latent change parameters
# -> key parameter is "d_selfes_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_selfes_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_selfes_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_selfes_1 ~1 | d_int_a | 0.866 | 1.886 | 5.675 | 0 |
| d_selfes_1 ~1 | d_int_b | 1.388 | 2.757 | 8.332 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_selfes_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_selfes_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_selfes_1 ~1 | d_int | 1.131 | 2.334 | 9.694 | 0 |
| d_selfes_1 ~1 | d_int | 1.131 | 2.356 | 9.694 | 0 |
# whole model (weak invariance)
summary(lcs_selfes_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 79 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 336 336
Self-Improvement 337 338
Number of missing patterns per group:
Self-Acceptance 2
Self-Improvement 2
Model Test User Model:
Standard Scaled
Test Statistic 17.124 16.149
Degrees of freedom 26 26
P-value (Chi-square) 0.905 0.933
Scaling correction factor 1.060
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 11.170 10.534
Self-Improvement 5.954 5.615
Model Test Baseline Model:
Test statistic 3063.748 2410.188
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.271
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.003 1.005
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.004
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3578.624 -3578.624
Scaling correction factor 0.727
for the MLR correction
Loglikelihood unrestricted model (H1) -3570.062 -3570.062
Scaling correction factor 1.103
for the MLR correction
Akaike (AIC) 7213.249 7213.249
Bayesian (BIC) 7339.578 7339.578
Sample-size adjusted Bayesian (SABIC) 7250.675 7250.675
Root Mean Square Error of Approximation:
RMSEA 0.000 0.000
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.018 0.010
P-value H_0: RMSEA <= 0.050 1.000 1.000
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.014
P-value H_0: Robust RMSEA <= 0.050 0.999
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.024 0.024
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t1 =~
slf_1_1 1.000 0.733 0.858
slf_2_1 (lmb2) 1.112 0.035 32.055 0.000 0.815 0.797
slf_3_1 (lmb3) 1.245 0.029 42.256 0.000 0.913 0.954
selfes_t2 =~
slf_1_2 1.000 0.747 0.862
slf_2_2 (lmb2) 1.112 0.035 32.055 0.000 0.831 0.802
slf_3_2 (lmb3) 1.245 0.029 42.256 0.000 0.930 0.956
d_selfes_1 =~
slfs_t2 1.000 0.615 0.615
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t2 ~
slfs_t1 1.000 0.982 0.982
d_selfes_1 ~
slfs_t1 (fb_a) -0.177 0.041 -4.336 0.000 -0.283 -0.283
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.selfes_par1_t1 ~~
.slf_1_2 (cov1) 0.076 0.016 4.662 0.000 0.076 0.394
.selfes_par2_t1 ~~
.slf_2_2 (cov2) 0.176 0.031 5.584 0.000 0.176 0.459
.selfes_par3_t1 ~~
.slf_3_2 (cov3) -0.035 0.017 -2.100 0.036 -0.035 -0.432
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_n_) 0.866 0.153 5.675 0.000 1.886 1.886
slfs_t1 (wb__) 3.531 0.046 76.740 0.000 4.815 4.815
.slf_1_1 0.000 0.000 0.000
.slf_2_1 (m2a) -1.200 0.133 -9.035 0.000 -1.200 -1.173
.slf_3_1 (m3a) -1.180 0.114 -10.337 0.000 -1.180 -1.233
.slf_1_2 0.000 0.000 0.000
.slf_2_2 (m2a) -1.200 0.133 -9.035 0.000 -1.200 -1.159
.slf_3_2 (m3a) -1.180 0.114 -10.337 0.000 -1.180 -1.212
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_v_) 0.194 0.029 6.762 0.000 0.920 0.920
slfs_t1 (wb__) 0.538 0.044 12.136 0.000 1.000 1.000
.slf_1_1 (res1) 0.192 0.017 11.333 0.000 0.192 0.263
.slf_2_1 (res2) 0.382 0.032 11.934 0.000 0.382 0.365
.slf_3_1 (res3) 0.082 0.017 4.799 0.000 0.082 0.089
.slf_1_2 (res1) 0.192 0.017 11.333 0.000 0.192 0.256
.slf_2_2 (res2) 0.382 0.032 11.934 0.000 0.382 0.357
.slf_3_2 (res3) 0.082 0.017 4.799 0.000 0.082 0.086
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t1 =~
slf_1_1 1.000 0.757 0.880
slf_2_1 (lmb2) 1.112 0.035 32.055 0.000 0.841 0.807
slf_3_1 (lmb3) 1.245 0.029 42.256 0.000 0.942 0.929
selfes_t2 =~
slf_1_2 1.000 0.686 0.859
slf_2_2 (lmb2) 1.112 0.035 32.055 0.000 0.763 0.778
slf_3_2 (lmb3) 1.245 0.029 42.256 0.000 0.855 0.916
d_selfes_1 =~
slfs_t2 1.000 0.734 0.734
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
selfes_t2 ~
slfs_t1 1.000 1.103 1.103
d_selfes_1 ~
slfs_t1 (fb_b) -0.310 0.043 -7.295 0.000 -0.466 -0.466
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.selfes_par1_t1 ~~
.slf_1_2 (cv1b) 0.065 0.016 4.023 0.000 0.065 0.388
.selfes_par2_t1 ~~
.slf_2_2 (cv2b) 0.137 0.027 5.051 0.000 0.137 0.359
.selfes_par3_t1 ~~
.slf_3_2 (cv3b) 0.034 0.024 1.433 0.152 0.034 0.246
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_n_) 1.388 0.167 8.332 0.000 2.757 2.757
slfs_t1 (wb__) 3.548 0.045 78.100 0.000 4.689 4.689
.slf_1_1 0.000 0.000 0.000
.slf_2_1 (m2b) -1.255 0.130 -9.655 0.000 -1.255 -1.203
.slf_3_1 (m3b) -1.185 0.114 -10.411 0.000 -1.185 -1.168
.slf_1_2 0.000 0.000 0.000
.slf_2_2 (m2b) -1.255 0.130 -9.655 0.000 -1.255 -1.280
.slf_3_2 (m3b) -1.185 0.114 -10.411 0.000 -1.185 -1.270
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.slfs_t2 0.000 0.000 0.000
.d_slf_1 (d_v_) 0.198 0.027 7.294 0.000 0.783 0.783
slfs_t1 (wb__) 0.573 0.044 12.915 0.000 1.000 1.000
.slf_1_1 (rs1b) 0.167 0.016 10.278 0.000 0.167 0.226
.slf_2_1 (rs2b) 0.380 0.029 13.294 0.000 0.380 0.350
.slf_3_1 (rs3b) 0.140 0.025 5.653 0.000 0.140 0.136
.slf_1_2 (rs1b) 0.167 0.016 10.278 0.000 0.167 0.262
.slf_2_2 (rs2b) 0.380 0.029 13.294 0.000 0.380 0.395
.slf_3_2 (rs3b) 0.140 0.025 5.653 0.000 0.140 0.161Self-concept clarity: fitting multi-group models
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)
# configural invariance
mi_lcs_concept_group_config <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2a", "lamb2b")*concept_par2_t1 + c("lamb3a", "lamb3b")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2a", "lamb2b")*concept_par2_t2 + c("lamb3a", "lamb3b")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_config <- sem(mi_lcs_concept_group_config, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr',
fixed.x=FALSE, missing='fiml', group = "rando")
# weak invariance
mi_lcs_concept_group_weak <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_weak <- sem(mi_lcs_concept_group_weak, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = "loadings")
# strong invariance
mi_lcs_concept_group_strong <- '
concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1
concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadings
concept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1
d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2
concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0
concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0
d_concept_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score
concept_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of concept_t1
d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores
concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1
d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameter
concept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2
concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2
concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2
concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1
concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1
concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1
concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2
concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2
concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2
concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1
concept_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1
concept_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1
concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2
concept_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2
concept_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2
'
lcs_concept_group_strong <- sem(mi_lcs_concept_group_strong, data=df_sbsa2_wide_wb %>% filter(rando!="Waitlist-Control"), estimator='mlr', fixed.x=FALSE, missing='fiml',
group = "rando", group.equal = c("intercepts", "loadings"))Meaning in life: results
# model comparison tests for measurement invariance
lavTestLRT(lcs_concept_group_config, lcs_concept_group_weak, lcs_concept_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_concept_group_config 24 7424.3 7559.7 31.207
lcs_concept_group_weak 26 7420.6 7546.9 31.474 0.26259 2 0.8770
lcs_concept_group_strong 30 7413.3 7521.6 32.179 0.69741 4 0.9516# show model with varying latent change parameters
# -> key parameter is "d_concept_1 ~1"
# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)
kable(broom::tidy(lcs_concept_group_weak, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_concept_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_concept_1 ~1 | d_int_a | 1.014 | 2.021 | 6.265 | 0 |
| d_concept_1 ~1 | d_int_b | 1.113 | 2.247 | 8.203 | 0 |
# constrained to be equal in the strong measurement invariance model:
kable(broom::tidy(lcs_concept_group_strong, conf.int = TRUE, conf.level = 0.95) %>%
select(term, label, estimate, std.all, statistic, p.value) %>%
filter(term %in% c("d_concept_1 ~1 ")), digits = 3) | term | label | estimate | std.all | statistic | p.value |
|---|---|---|---|---|---|
| d_concept_1 ~1 | d_int | 1.07 | 2.111 | 10.268 | 0 |
| d_concept_1 ~1 | d_int | 1.07 | 2.181 | 10.268 | 0 |
# whole model (weak invariance)
summary(lcs_concept_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)lavaan 0.6.15 ended normally after 65 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 44
Number of equality constraints 16
Number of observations per group: Used Total
Self-Acceptance 336 336
Self-Improvement 337 338
Number of missing patterns per group:
Self-Acceptance 2
Self-Improvement 2
Model Test User Model:
Standard Scaled
Test Statistic 31.474 28.712
Degrees of freedom 26 26
P-value (Chi-square) 0.211 0.324
Scaling correction factor 1.096
Yuan-Bentler correction (Mplus variant)
Test statistic for each group:
Self-Acceptance 17.734 16.178
Self-Improvement 13.740 12.534
Model Test Baseline Model:
Test statistic 2887.162 2162.733
Degrees of freedom 30 30
P-value 0.000 0.000
Scaling correction factor 1.335
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.998 0.999
Tucker-Lewis Index (TLI) 0.998 0.999
Robust Comparative Fit Index (CFI) 0.999
Robust Tucker-Lewis Index (TLI) 0.999
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -3682.288 -3682.288
Scaling correction factor 0.726
for the MLR correction
Loglikelihood unrestricted model (H1) -3666.551 -3666.551
Scaling correction factor 1.119
for the MLR correction
Akaike (AIC) 7420.576 7420.576
Bayesian (BIC) 7546.905 7546.905
Sample-size adjusted Bayesian (SABIC) 7458.003 7458.003
Root Mean Square Error of Approximation:
RMSEA 0.025 0.018
90 Percent confidence interval - lower 0.000 0.000
90 Percent confidence interval - upper 0.052 0.047
P-value H_0: RMSEA <= 0.050 0.932 0.971
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.020
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.053
P-value H_0: Robust RMSEA <= 0.050 0.925
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.022 0.022
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Group 1 [Self-Acceptance]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t1 =~
cnc_1_1 1.000 0.650 0.794
cnc_2_1 (lmb2) 1.283 0.041 31.361 0.000 0.834 0.869
cnc_3_1 (lmb3) 1.414 0.042 33.772 0.000 0.919 0.924
concept_t2 =~
cnc_1_2 1.000 0.663 0.800
cnc_2_2 (lmb2) 1.283 0.041 31.361 0.000 0.851 0.873
cnc_3_2 (lmb3) 1.414 0.042 33.772 0.000 0.938 0.927
d_concept_1 =~
cncpt_2 1.000 0.756 0.756
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t2 ~
cncpt_1 1.000 0.980 0.980
d_concept_1 ~
cncpt_1 (fb_a) -0.277 0.051 -5.471 0.000 -0.359 -0.359
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.concept_par1_t1 ~~
.cnc_1_2 (cov1) 0.070 0.020 3.400 0.001 0.070 0.281
.concept_par2_t1 ~~
.cnc_2_2 (cov2) 0.086 0.021 4.071 0.000 0.086 0.380
.concept_par3_t1 ~~
.cnc_3_2 (cov3) 0.028 0.022 1.296 0.195 0.028 0.194
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_n_) 1.014 0.162 6.265 0.000 2.021 2.021
cncpt_1 (wb__) 3.050 0.042 72.638 0.000 4.692 4.692
.cnc_1_1 0.000 0.000 0.000
.cnc_2_1 (m2a) -0.931 0.138 -6.754 0.000 -0.931 -0.970
.cnc_3_1 (m3a) -1.377 0.138 -9.984 0.000 -1.377 -1.384
.cnc_1_2 0.000 0.000 0.000
.cnc_2_2 (m2a) -0.931 0.138 -6.754 0.000 -0.931 -0.955
.cnc_3_2 (m3a) -1.377 0.138 -9.984 0.000 -1.377 -1.361
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_v_) 0.219 0.026 8.587 0.000 0.871 0.871
cncpt_1 (wb__) 0.423 0.036 11.577 0.000 1.000 1.000
.cnc_1_1 (res1) 0.248 0.020 12.590 0.000 0.248 0.370
.cnc_2_1 (res2) 0.227 0.020 11.576 0.000 0.227 0.246
.cnc_3_1 (res3) 0.144 0.023 6.215 0.000 0.144 0.146
.cnc_1_2 (res1) 0.248 0.020 12.590 0.000 0.248 0.361
.cnc_2_2 (res2) 0.227 0.020 11.576 0.000 0.227 0.238
.cnc_3_2 (res3) 0.144 0.023 6.215 0.000 0.144 0.141
Group 2 [Self-Improvement]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t1 =~
cnc_1_1 1.000 0.700 0.802
cnc_2_1 (lmb2) 1.283 0.041 31.361 0.000 0.899 0.895
cnc_3_1 (lmb3) 1.414 0.042 33.772 0.000 0.990 0.919
concept_t2 =~
cnc_1_2 1.000 0.661 0.785
cnc_2_2 (lmb2) 1.283 0.041 31.361 0.000 0.848 0.885
cnc_3_2 (lmb3) 1.414 0.042 33.772 0.000 0.934 0.910
d_concept_1 =~
cncpt_2 1.000 0.750 0.750
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
concept_t2 ~
cncpt_1 1.000 1.060 1.060
d_concept_1 ~
cncpt_1 (fb_b) -0.305 0.042 -7.249 0.000 -0.432 -0.432
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.concept_par1_t1 ~~
.cnc_1_2 (cv1b) 0.072 0.020 3.556 0.000 0.072 0.263
.concept_par2_t1 ~~
.cnc_2_2 (cv2b) 0.063 0.020 3.196 0.001 0.063 0.315
.concept_par3_t1 ~~
.cnc_3_2 (cv3b) 0.037 0.021 1.798 0.072 0.037 0.207
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_n_) 1.113 0.136 8.203 0.000 2.247 2.247
cncpt_1 (wb__) 3.038 0.045 67.515 0.000 4.337 4.337
.cnc_1_1 0.000 0.000 0.000
.cnc_2_1 (m2b) -0.899 0.134 -6.729 0.000 -0.899 -0.896
.cnc_3_1 (m3b) -1.363 0.135 -10.067 0.000 -1.363 -1.265
.cnc_1_2 0.000 0.000 0.000
.cnc_2_2 (m2b) -0.899 0.134 -6.729 0.000 -0.899 -0.939
.cnc_3_2 (m3b) -1.363 0.135 -10.067 0.000 -1.363 -1.328
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.cncpt_2 0.000 0.000 0.000
.d_cnc_1 (d_v_) 0.200 0.029 6.986 0.000 0.814 0.814
cncpt_1 (wb__) 0.490 0.039 12.493 0.000 1.000 1.000
.cnc_1_1 (rs1b) 0.273 0.020 13.575 0.000 0.273 0.357
.cnc_2_1 (rs2b) 0.199 0.021 9.657 0.000 0.199 0.198
.cnc_3_1 (rs3b) 0.181 0.027 6.668 0.000 0.181 0.156
.cnc_1_2 (rs1b) 0.273 0.020 13.575 0.000 0.273 0.385
.cnc_2_2 (rs2b) 0.199 0.021 9.657 0.000 0.199 0.217
.cnc_3_2 (rs3b) 0.181 0.027 6.668 0.000 0.181 0.172Profile correlations by group and measurement occasion (mixed effects models) - results:
df_sbsa2_t1t2 <- df_sbsa2 %>% filter(rando!="Waitlist-Control" & time %in% c(1,2)) %>% mutate(time_d = time - 1)
psych::describeBy(df_sbsa2_t1t2$profile_corr_item_z, list(df_sbsa2_t1t2$rando, df_sbsa2_t1t2$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 335 0.42 0.49 0.37 0.38 0.43 -1.09 3.39 4.48 1.14 4.25 0.03
------------------------------------------------------------
: Self-Improvement
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 336 0.42 0.49 0.39 0.4 0.51 -0.89 2.33 3.22 0.47 0.36 0.03
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 284 0.55 0.5 0.52 0.53 0.48 -1.07 2.17 3.23 0.36 0.5 0.03
------------------------------------------------------------
: Self-Improvement
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 291 0.56 0.48 0.54 0.55 0.56 -0.62 1.83 2.45 0.19 -0.53 0.03psych::describeBy(df_sbsa2_t1t2$profile_corr_facet_z, list(df_sbsa2_t1t2$rando, df_sbsa2_t1t2$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 335 0.35 0.65 0.27 0.31 0.6 -1.07 3.77 4.84 0.93 2.14 0.04
------------------------------------------------------------
: Self-Improvement
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 337 0.34 0.67 0.29 0.31 0.7 -1.23 2.53 3.75 0.46 0.04 0.04
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 284 0.51 0.67 0.46 0.48 0.66 -1.34 3.09 4.43 0.42 0.32 0.04
------------------------------------------------------------
: Self-Improvement
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 292 0.53 0.66 0.49 0.5 0.69 -1.08 2.51 3.6 0.32 -0.32 0.04lm_profile_corr_item <- lmerTest::lmer(profile_corr_item_z ~ time_d * rando + (1 | pid), data = df_sbsa2_t1t2)
lm_profile_corr_facet <- lmerTest::lmer(profile_corr_facet_z ~ time_d * rando + (1 | pid), data = df_sbsa2_t1t2)
kable(bind_rows(as.data.frame(summary(lm_profile_corr_item)$coefficients), as.data.frame(summary(lm_profile_corr_facet)$coefficients)) %>% as_tibble() %>% mutate(outcome = c(rep("Item-level profile corr.", 4), rep("Facet-level profile corr.", 4)), term = c(rep(c("Intercept", "time", "group", "time*group"), 2))) %>% select(outcome, term, everything()) %>% rename(p = `Pr(>|t|)`), digits = 3)| outcome | term | Estimate | Std. Error | df | t value | p |
|---|---|---|---|---|---|---|
| Item-level profile corr. | Intercept | 0.418 | 0.027 | 845.076 | 15.580 | 0.000 |
| Item-level profile corr. | time | 0.129 | 0.021 | 592.195 | 6.065 | 0.000 |
| Item-level profile corr. | group | 0.000 | 0.038 | 845.887 | -0.012 | 0.990 |
| Item-level profile corr. | time*group | 0.007 | 0.030 | 591.350 | 0.242 | 0.809 |
| Facet-level profile corr. | Intercept | 0.350 | 0.036 | 846.010 | 9.723 | 0.000 |
| Facet-level profile corr. | time | 0.164 | 0.028 | 595.605 | 5.783 | 0.000 |
| Facet-level profile corr. | group | -0.008 | 0.051 | 846.005 | -0.151 | 0.880 |
| Facet-level profile corr. | time*group | 0.010 | 0.040 | 594.236 | 0.259 | 0.796 |
Squared trait-/facet-differences by group and measurement occasion (mixed effects models) - results:
mod_traits_sqdiff_groups <- df_sbsa2_t1t2 %>%
select(pid, time_d, rando, ends_with("_sqdiff")) %>%
pivot_longer(ends_with("_sqdiff"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_by(pid, test) %>%
mutate(assessments = n()) %>%
ungroup() %>%
filter(assessments==2) %>%
select(-assessments) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
mod_traits_sqdiff_groups_unlist <- as.data.frame(summary(mod_traits_sqdiff_groups[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(mod_traits_sqdiff_groups)) {
mod_traits_sqdiff_groups_unlist <- bind_rows(mod_traits_sqdiff_groups_unlist,
as.data.frame(summary(mod_traits_sqdiff_groups[[i]])$coefficients) %>% as_tibble())
}
kable(mod_traits_sqdiff_groups_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
select(outcome, term, everything()) %>% rename(p = `Pr(>|t|)`) %>%
arrange(factor(outcome, levels = (names(b5_vars)))),
digits = 3)| outcome | term | Estimate | Std. Error | df | t value | p |
|---|---|---|---|---|---|---|
| extraversion | Intercept | 1.753 | 0.108 | 768.868 | 16.160 | 0.000 |
| extraversion | time | -0.308 | 0.083 | 575.000 | -3.693 | 0.000 |
| extraversion | group | 0.172 | 0.152 | 768.868 | 1.128 | 0.259 |
| extraversion | time*group | -0.126 | 0.117 | 575.000 | -1.075 | 0.283 |
| agreeableness | Intercept | 0.507 | 0.048 | 794.537 | 10.552 | 0.000 |
| agreeableness | time | -0.078 | 0.039 | 574.000 | -1.999 | 0.046 |
| agreeableness | group | -0.032 | 0.067 | 794.537 | -0.477 | 0.633 |
| agreeableness | time*group | -0.037 | 0.055 | 574.000 | -0.678 | 0.498 |
| conscientiousness | Intercept | 1.661 | 0.113 | 752.187 | 14.681 | 0.000 |
| conscientiousness | time | -0.139 | 0.084 | 575.000 | -1.662 | 0.097 |
| conscientiousness | group | -0.067 | 0.159 | 752.187 | -0.421 | 0.674 |
| conscientiousness | time*group | -0.060 | 0.117 | 575.000 | -0.510 | 0.610 |
| neuroticism | Intercept | 3.363 | 0.189 | 766.422 | 17.764 | 0.000 |
| neuroticism | time | -0.636 | 0.145 | 575.000 | -4.390 | 0.000 |
| neuroticism | group | -0.018 | 0.266 | 766.422 | -0.068 | 0.946 |
| neuroticism | time*group | -0.121 | 0.203 | 575.000 | -0.594 | 0.553 |
| openness | Intercept | 0.464 | 0.048 | 823.546 | 9.580 | 0.000 |
| openness | time | -0.054 | 0.042 | 573.000 | -1.288 | 0.198 |
| openness | group | 0.002 | 0.068 | 823.546 | 0.029 | 0.977 |
| openness | time*group | -0.043 | 0.059 | 573.000 | -0.738 | 0.461 |
| sociability | Intercept | 2.989 | 0.179 | 797.073 | 16.696 | 0.000 |
| sociability | time | -0.870 | 0.147 | 573.000 | -5.907 | 0.000 |
| sociability | group | -0.016 | 0.251 | 797.073 | -0.066 | 0.948 |
| sociability | time*group | 0.173 | 0.207 | 573.000 | 0.835 | 0.404 |
| assertiveness | Intercept | 1.601 | 0.130 | 819.490 | 12.291 | 0.000 |
| assertiveness | time | -0.116 | 0.112 | 572.000 | -1.034 | 0.302 |
| assertiveness | group | 0.304 | 0.183 | 819.490 | 1.663 | 0.097 |
| assertiveness | time*group | -0.243 | 0.157 | 572.000 | -1.540 | 0.124 |
| energy | Intercept | 1.976 | 0.145 | 870.043 | 13.607 | 0.000 |
| energy | time | -0.244 | 0.135 | 574.000 | -1.803 | 0.072 |
| energy | group | 0.127 | 0.204 | 870.043 | 0.624 | 0.533 |
| energy | time*group | -0.182 | 0.190 | 574.000 | -0.959 | 0.338 |
| compassion | Intercept | 0.476 | 0.059 | 938.372 | 8.086 | 0.000 |
| compassion | time | -0.053 | 0.060 | 574.000 | -0.870 | 0.385 |
| compassion | group | 0.139 | 0.083 | 938.372 | 1.684 | 0.093 |
| compassion | time*group | -0.128 | 0.085 | 574.000 | -1.511 | 0.131 |
| respectfulness | Intercept | 0.520 | 0.061 | 825.639 | 8.474 | 0.000 |
| respectfulness | time | 0.007 | 0.054 | 571.000 | 0.130 | 0.897 |
| respectfulness | group | -0.025 | 0.086 | 825.639 | -0.290 | 0.772 |
| respectfulness | time*group | -0.038 | 0.075 | 571.000 | -0.499 | 0.618 |
| trust | Intercept | 1.239 | 0.094 | 934.116 | 13.146 | 0.000 |
| trust | time | -0.309 | 0.096 | 573.000 | -3.204 | 0.001 |
| trust | group | -0.123 | 0.133 | 934.116 | -0.930 | 0.353 |
| trust | time*group | -0.039 | 0.136 | 573.000 | -0.289 | 0.773 |
| organization | Intercept | 1.852 | 0.146 | 796.417 | 12.680 | 0.000 |
| organization | time | -0.143 | 0.120 | 574.000 | -1.199 | 0.231 |
| organization | group | -0.165 | 0.205 | 796.417 | -0.806 | 0.421 |
| organization | time*group | -0.011 | 0.168 | 574.000 | -0.066 | 0.947 |
| productiveness | Intercept | 2.834 | 0.171 | 802.138 | 16.604 | 0.000 |
| productiveness | time | -0.355 | 0.143 | 571.000 | -2.488 | 0.013 |
| productiveness | group | 0.018 | 0.240 | 802.138 | 0.074 | 0.941 |
| productiveness | time*group | -0.194 | 0.200 | 571.000 | -0.966 | 0.335 |
| responsibility | Intercept | 1.284 | 0.117 | 792.347 | 10.981 | 0.000 |
| responsibility | time | -0.048 | 0.096 | 572.000 | -0.499 | 0.618 |
| responsibility | group | 0.060 | 0.164 | 792.347 | 0.364 | 0.716 |
| responsibility | time*group | -0.179 | 0.134 | 572.000 | -1.337 | 0.182 |
| anxiety | Intercept | 4.276 | 0.252 | 810.764 | 17.001 | 0.000 |
| anxiety | time | -0.620 | 0.211 | 575.000 | -2.934 | 0.003 |
| anxiety | group | 0.051 | 0.353 | 810.764 | 0.144 | 0.886 |
| anxiety | time*group | -0.119 | 0.297 | 575.000 | -0.401 | 0.689 |
| depression | Intercept | 3.968 | 0.220 | 759.626 | 18.006 | 0.000 |
| depression | time | -0.894 | 0.167 | 573.000 | -5.356 | 0.000 |
| depression | group | -0.076 | 0.310 | 759.626 | -0.246 | 0.805 |
| depression | time*group | 0.013 | 0.235 | 573.000 | 0.056 | 0.955 |
| volatility | Intercept | 2.821 | 0.199 | 851.931 | 14.187 | 0.000 |
| volatility | time | -0.464 | 0.180 | 575.000 | -2.580 | 0.010 |
| volatility | group | 0.041 | 0.279 | 851.931 | 0.149 | 0.882 |
| volatility | time*group | -0.275 | 0.252 | 575.000 | -1.088 | 0.277 |
| curiosity | Intercept | 0.558 | 0.065 | 870.775 | 8.598 | 0.000 |
| curiosity | time | -0.087 | 0.061 | 572.000 | -1.431 | 0.153 |
| curiosity | group | -0.003 | 0.091 | 870.775 | -0.038 | 0.970 |
| curiosity | time*group | 0.084 | 0.085 | 572.000 | 0.985 | 0.325 |
| aesthetic | Intercept | 0.440 | 0.054 | 934.046 | 8.107 | 0.000 |
| aesthetic | time | -0.045 | 0.056 | 571.000 | -0.804 | 0.422 |
| aesthetic | group | 0.011 | 0.076 | 934.046 | 0.150 | 0.880 |
| aesthetic | time*group | -0.017 | 0.078 | 571.000 | -0.211 | 0.833 |
| imagination | Intercept | 1.173 | 0.108 | 781.107 | 10.813 | 0.000 |
| imagination | time | -0.201 | 0.086 | 573.000 | -2.333 | 0.020 |
| imagination | group | 0.006 | 0.152 | 781.107 | 0.037 | 0.971 |
| imagination | time*group | -0.027 | 0.121 | 573.000 | -0.227 | 0.821 |
Now confirmatory in Study 2.
We will explore b) whether the extent of change/acceptance is related to personal project dimension variables.
Not assessed, anymore, in study 2. Please see study 1 report for results.
We will explore c) whether there are stronger rates of change/acceptance on traits that were selected as change goals than those that were not.
TBD!
Does self-improvement intervene on current self trait change (but not ideal self)? Does self-acceptance intervene on ideal self trait change (but not current self)?
Changes in current self traits over time:
df_manip_check_curr <- df_sbsa2_t1t2 %>%
select(pid, time_d, rando,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_curr")) %>%
pivot_longer(ends_with("_comb_curr"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_manip_check_curr_unlist <- as.data.frame(summary(df_manip_check_curr[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(df_manip_check_curr)) {
df_manip_check_curr_unlist <- bind_rows(df_manip_check_curr_unlist,
as.data.frame(summary(df_manip_check_curr[[i]])$coefficients) %>% as_tibble())
}kable(df_manip_check_curr_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
rename(p = `Pr(>|t|)`, str_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, str_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | est | str_err | p | sig |
|---|---|---|---|---|---|
| extraversion | Intercept | 2.845 | 0.039 | 0.000 | *** |
| extraversion | time | 0.113 | 0.026 | 0.000 | *** |
| extraversion | group | -0.069 | 0.054 | 0.203 | |
| extraversion | time*group | 0.009 | 0.036 | 0.800 | |
| agreeableness | Intercept | 3.633 | 0.032 | 0.000 | *** |
| agreeableness | time | 0.048 | 0.023 | 0.038 | * |
| agreeableness | group | -0.030 | 0.046 | 0.515 | |
| agreeableness | time*group | 0.033 | 0.033 | 0.316 | |
| conscientiousness | Intercept | 3.371 | 0.042 | 0.000 | *** |
| conscientiousness | time | 0.075 | 0.027 | 0.006 | ** |
| conscientiousness | group | 0.026 | 0.059 | 0.653 | |
| conscientiousness | time*group | 0.026 | 0.038 | 0.490 | |
| neuroticism | Intercept | 3.220 | 0.046 | 0.000 | *** |
| neuroticism | time | -0.172 | 0.030 | 0.000 | *** |
| neuroticism | group | 0.033 | 0.065 | 0.611 | |
| neuroticism | time*group | -0.006 | 0.042 | 0.887 | |
| openness | Intercept | 3.707 | 0.035 | 0.000 | *** |
| openness | time | 0.079 | 0.023 | 0.001 | *** |
| openness | group | -0.029 | 0.050 | 0.555 | |
| openness | time*group | -0.001 | 0.032 | 0.977 | |
| sociability | Intercept | 2.485 | 0.054 | 0.000 | *** |
| sociability | time | 0.168 | 0.037 | 0.000 | *** |
| sociability | group | -0.040 | 0.076 | 0.600 | |
| sociability | time*group | -0.061 | 0.053 | 0.244 | |
| assertiveness | Intercept | 2.908 | 0.048 | 0.000 | *** |
| assertiveness | time | 0.076 | 0.035 | 0.028 | * |
| assertiveness | group | -0.078 | 0.068 | 0.250 | |
| assertiveness | time*group | 0.036 | 0.048 | 0.453 | |
| energy | Intercept | 3.140 | 0.046 | 0.000 | *** |
| energy | time | 0.094 | 0.038 | 0.014 | * |
| energy | group | -0.088 | 0.065 | 0.176 | |
| energy | time*group | 0.053 | 0.054 | 0.326 | |
| compassion | Intercept | 3.753 | 0.041 | 0.000 | *** |
| compassion | time | 0.042 | 0.037 | 0.254 | |
| compassion | group | -0.056 | 0.057 | 0.326 | |
| compassion | time*group | 0.079 | 0.051 | 0.124 | |
| respectfulness | Intercept | 4.035 | 0.038 | 0.000 | *** |
| respectfulness | time | 0.013 | 0.032 | 0.680 | |
| respectfulness | group | -0.014 | 0.054 | 0.804 | |
| respectfulness | time*group | -0.002 | 0.044 | 0.955 | |
| trust | Intercept | 3.110 | 0.045 | 0.000 | *** |
| trust | time | 0.097 | 0.035 | 0.006 | ** |
| trust | group | -0.016 | 0.064 | 0.800 | |
| trust | time*group | 0.011 | 0.049 | 0.831 | |
| organization | Intercept | 3.445 | 0.052 | 0.000 | *** |
| organization | time | 0.056 | 0.037 | 0.131 | |
| organization | group | 0.109 | 0.074 | 0.143 | |
| organization | time*group | 0.023 | 0.052 | 0.657 | |
| productiveness | Intercept | 3.148 | 0.049 | 0.000 | *** |
| productiveness | time | 0.141 | 0.039 | 0.000 | *** |
| productiveness | group | 0.043 | 0.069 | 0.538 | |
| productiveness | time*group | 0.001 | 0.054 | 0.978 | |
| responsibility | Intercept | 3.522 | 0.045 | 0.000 | *** |
| responsibility | time | 0.035 | 0.036 | 0.333 | |
| responsibility | group | -0.068 | 0.064 | 0.284 | |
| responsibility | time*group | 0.052 | 0.051 | 0.311 | |
| anxiety | Intercept | 3.525 | 0.050 | 0.000 | *** |
| anxiety | time | -0.147 | 0.037 | 0.000 | *** |
| anxiety | group | 0.093 | 0.070 | 0.185 | |
| anxiety | time*group | -0.012 | 0.052 | 0.817 | |
| depression | Intercept | 3.144 | 0.054 | 0.000 | *** |
| depression | time | -0.238 | 0.039 | 0.000 | *** |
| depression | group | 0.004 | 0.076 | 0.954 | |
| depression | time*group | 0.031 | 0.055 | 0.572 | |
| volatility | Intercept | 2.993 | 0.054 | 0.000 | *** |
| volatility | time | -0.127 | 0.042 | 0.002 | ** |
| volatility | group | 0.001 | 0.076 | 0.987 | |
| volatility | time*group | -0.045 | 0.058 | 0.441 | |
| curiosity | Intercept | 3.817 | 0.043 | 0.000 | *** |
| curiosity | time | 0.052 | 0.032 | 0.106 | |
| curiosity | group | -0.014 | 0.060 | 0.817 | |
| curiosity | time*group | 0.006 | 0.045 | 0.899 | |
| aesthetic | Intercept | 3.661 | 0.047 | 0.000 | *** |
| aesthetic | time | 0.047 | 0.036 | 0.192 | |
| aesthetic | group | -0.087 | 0.066 | 0.185 | |
| aesthetic | time*group | 0.036 | 0.050 | 0.474 | |
| imagination | Intercept | 3.642 | 0.045 | 0.000 | *** |
| imagination | time | 0.141 | 0.035 | 0.000 | *** |
| imagination | group | 0.014 | 0.063 | 0.824 | |
| imagination | time*group | -0.046 | 0.049 | 0.349 |
Changes in ideal self traits over time:
df_manip_check_ideal <- df_sbsa2_t1t2 %>%
select(pid, time_d, rando,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_ideal")) %>%
pivot_longer(ends_with("_comb_ideal"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_manip_check_ideal_unlist <- as.data.frame(summary(df_manip_check_ideal[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(df_manip_check_ideal)) {
df_manip_check_ideal_unlist <- bind_rows(df_manip_check_ideal_unlist,
as.data.frame(summary(df_manip_check_ideal[[i]])$coefficients) %>% as_tibble())
}kable(df_manip_check_ideal_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
rename(p = `Pr(>|t|)`, str_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, str_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | est | str_err | p | sig |
|---|---|---|---|---|---|
| extraversion | Intercept | 3.919 | 0.027 | 0.000 | *** |
| extraversion | time | -0.023 | 0.024 | 0.351 | |
| extraversion | group | -0.009 | 0.038 | 0.812 | |
| extraversion | time*group | 0.007 | 0.034 | 0.827 | |
| agreeableness | Intercept | 4.078 | 0.029 | 0.000 | *** |
| agreeableness | time | 0.007 | 0.024 | 0.777 | |
| agreeableness | group | -0.018 | 0.041 | 0.670 | |
| agreeableness | time*group | -0.035 | 0.034 | 0.300 | |
| conscientiousness | Intercept | 4.414 | 0.027 | 0.000 | *** |
| conscientiousness | time | 0.027 | 0.024 | 0.266 | |
| conscientiousness | group | 0.005 | 0.038 | 0.889 | |
| conscientiousness | time*group | -0.018 | 0.034 | 0.601 | |
| neuroticism | Intercept | 1.704 | 0.028 | 0.000 | *** |
| neuroticism | time | 0.004 | 0.026 | 0.878 | |
| neuroticism | group | -0.032 | 0.039 | 0.413 | |
| neuroticism | time*group | 0.032 | 0.037 | 0.381 | |
| openness | Intercept | 4.140 | 0.031 | 0.000 | *** |
| openness | time | 0.054 | 0.025 | 0.035 | * |
| openness | group | -0.052 | 0.043 | 0.233 | |
| openness | time*group | 0.001 | 0.036 | 0.971 | |
| sociability | Intercept | 3.735 | 0.038 | 0.000 | *** |
| sociability | time | -0.085 | 0.036 | 0.020 | * |
| sociability | group | -0.029 | 0.054 | 0.583 | |
| sociability | time*group | 0.060 | 0.051 | 0.239 | |
| assertiveness | Intercept | 3.832 | 0.037 | 0.000 | *** |
| assertiveness | time | 0.001 | 0.035 | 0.984 | |
| assertiveness | group | 0.001 | 0.052 | 0.981 | |
| assertiveness | time*group | 0.002 | 0.049 | 0.967 | |
| energy | Intercept | 4.189 | 0.034 | 0.000 | *** |
| energy | time | 0.016 | 0.038 | 0.672 | |
| energy | group | 0.003 | 0.048 | 0.953 | |
| energy | time*group | -0.047 | 0.053 | 0.384 | |
| compassion | Intercept | 4.029 | 0.038 | 0.000 | *** |
| compassion | time | 0.016 | 0.037 | 0.675 | |
| compassion | group | -0.019 | 0.053 | 0.721 | |
| compassion | time*group | -0.008 | 0.052 | 0.885 | |
| respectfulness | Intercept | 4.454 | 0.032 | 0.000 | *** |
| respectfulness | time | -0.001 | 0.029 | 0.963 | |
| respectfulness | group | -0.031 | 0.045 | 0.492 | |
| respectfulness | time*group | -0.007 | 0.041 | 0.862 | |
| trust | Intercept | 3.751 | 0.040 | 0.000 | *** |
| trust | time | 0.013 | 0.036 | 0.728 | |
| trust | group | -0.002 | 0.056 | 0.965 | |
| trust | time*group | -0.099 | 0.051 | 0.053 | |
| organization | Intercept | 4.426 | 0.032 | 0.000 | *** |
| organization | time | 0.006 | 0.034 | 0.849 | |
| organization | group | 0.001 | 0.045 | 0.982 | |
| organization | time*group | 0.031 | 0.048 | 0.511 | |
| productiveness | Intercept | 4.513 | 0.030 | 0.000 | *** |
| productiveness | time | 0.045 | 0.030 | 0.137 | |
| productiveness | group | 0.055 | 0.042 | 0.197 | |
| productiveness | time*group | -0.052 | 0.042 | 0.215 | |
| responsibility | Intercept | 4.300 | 0.035 | 0.000 | *** |
| responsibility | time | 0.035 | 0.033 | 0.288 | |
| responsibility | group | -0.031 | 0.049 | 0.523 | |
| responsibility | time*group | -0.027 | 0.046 | 0.562 | |
| anxiety | Intercept | 1.876 | 0.035 | 0.000 | *** |
| anxiety | time | 0.030 | 0.039 | 0.445 | |
| anxiety | group | -0.036 | 0.050 | 0.466 | |
| anxiety | time*group | 0.004 | 0.055 | 0.947 | |
| depression | Intercept | 1.499 | 0.030 | 0.000 | *** |
| depression | time | -0.003 | 0.031 | 0.911 | |
| depression | group | -0.022 | 0.042 | 0.597 | |
| depression | time*group | 0.040 | 0.043 | 0.356 | |
| volatility | Intercept | 1.736 | 0.035 | 0.000 | *** |
| volatility | time | -0.018 | 0.036 | 0.611 | |
| volatility | group | -0.041 | 0.049 | 0.409 | |
| volatility | time*group | 0.066 | 0.050 | 0.188 | |
| curiosity | Intercept | 4.129 | 0.038 | 0.000 | *** |
| curiosity | time | 0.028 | 0.038 | 0.455 | |
| curiosity | group | -0.046 | 0.054 | 0.396 | |
| curiosity | time*group | 0.002 | 0.053 | 0.965 | |
| aesthetic | Intercept | 3.922 | 0.039 | 0.000 | *** |
| aesthetic | time | 0.108 | 0.037 | 0.004 | ** |
| aesthetic | group | -0.082 | 0.056 | 0.142 | |
| aesthetic | time*group | -0.050 | 0.052 | 0.334 | |
| imagination | Intercept | 4.369 | 0.036 | 0.000 | *** |
| imagination | time | 0.024 | 0.036 | 0.506 | |
| imagination | group | -0.010 | 0.051 | 0.842 | |
| imagination | time*group | 0.028 | 0.051 | 0.585 |
Changes in current self traits over time:
df_sbsa2_t2t3 <- df_sbsa2 %>% filter(rando!="Waitlist-Control" & time %in% c(2,3)) %>% mutate(time_d = time - 2)
df_manip_check_curr_fu <- df_sbsa2_t2t3 %>%
select(pid, time_d, rando,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_curr")) %>%
pivot_longer(ends_with("_comb_curr"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_manip_check_curr_fu_unlist <- as.data.frame(summary(df_manip_check_curr_fu[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(df_manip_check_curr_fu)) {
df_manip_check_curr_fu_unlist <- bind_rows(df_manip_check_curr_fu_unlist,
as.data.frame(summary(df_manip_check_curr_fu[[i]])$coefficients) %>% as_tibble())
}kable(df_manip_check_curr_fu_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
rename(p = `Pr(>|t|)`, str_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, str_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | est | str_err | p | sig |
|---|---|---|---|---|---|
| extraversion | Intercept | 2.951 | 0.041 | 0.000 | *** |
| extraversion | time | 0.032 | 0.025 | 0.217 | |
| extraversion | group | -0.050 | 0.058 | 0.389 | |
| extraversion | time*group | -0.017 | 0.036 | 0.625 | |
| agreeableness | Intercept | 3.697 | 0.035 | 0.000 | *** |
| agreeableness | time | 0.015 | 0.022 | 0.486 | |
| agreeableness | group | -0.011 | 0.050 | 0.822 | |
| agreeableness | time*group | -0.019 | 0.031 | 0.531 | |
| conscientiousness | Intercept | 3.468 | 0.045 | 0.000 | *** |
| conscientiousness | time | 0.072 | 0.028 | 0.009 | ** |
| conscientiousness | group | 0.067 | 0.064 | 0.296 | |
| conscientiousness | time*group | -0.060 | 0.039 | 0.121 | |
| neuroticism | Intercept | 3.068 | 0.049 | 0.000 | *** |
| neuroticism | time | -0.062 | 0.028 | 0.030 | * |
| neuroticism | group | -0.015 | 0.069 | 0.829 | |
| neuroticism | time*group | 0.017 | 0.040 | 0.674 | |
| openness | Intercept | 3.792 | 0.039 | 0.000 | *** |
| openness | time | -0.001 | 0.022 | 0.969 | |
| openness | group | -0.038 | 0.054 | 0.484 | |
| openness | time*group | 0.016 | 0.031 | 0.619 | |
| sociability | Intercept | 2.641 | 0.057 | 0.000 | *** |
| sociability | time | 0.046 | 0.036 | 0.202 | |
| sociability | group | -0.092 | 0.080 | 0.254 | |
| sociability | time*group | 0.006 | 0.050 | 0.902 | |
| assertiveness | Intercept | 2.984 | 0.051 | 0.000 | *** |
| assertiveness | time | 0.039 | 0.036 | 0.281 | |
| assertiveness | group | -0.050 | 0.072 | 0.484 | |
| assertiveness | time*group | -0.023 | 0.050 | 0.648 | |
| energy | Intercept | 3.228 | 0.048 | 0.000 | *** |
| energy | time | 0.011 | 0.040 | 0.781 | |
| energy | group | -0.008 | 0.068 | 0.905 | |
| energy | time*group | -0.034 | 0.056 | 0.535 | |
| compassion | Intercept | 3.814 | 0.044 | 0.000 | *** |
| compassion | time | 0.040 | 0.037 | 0.277 | |
| compassion | group | 0.000 | 0.062 | 1.000 | |
| compassion | time*group | -0.053 | 0.052 | 0.301 | |
| respectfulness | Intercept | 4.067 | 0.041 | 0.000 | *** |
| respectfulness | time | 0.025 | 0.029 | 0.394 | |
| respectfulness | group | -0.017 | 0.058 | 0.772 | |
| respectfulness | time*group | -0.011 | 0.041 | 0.791 | |
| trust | Intercept | 3.211 | 0.048 | 0.000 | *** |
| trust | time | -0.018 | 0.035 | 0.603 | |
| trust | group | -0.019 | 0.068 | 0.780 | |
| trust | time*group | 0.010 | 0.048 | 0.834 | |
| organization | Intercept | 3.520 | 0.056 | 0.000 | *** |
| organization | time | 0.077 | 0.037 | 0.038 | * |
| organization | group | 0.153 | 0.079 | 0.053 | |
| organization | time*group | -0.027 | 0.052 | 0.607 | |
| productiveness | Intercept | 3.297 | 0.054 | 0.000 | *** |
| productiveness | time | 0.077 | 0.037 | 0.039 | * |
| productiveness | group | 0.064 | 0.076 | 0.402 | |
| productiveness | time*group | -0.091 | 0.052 | 0.080 | |
| responsibility | Intercept | 3.586 | 0.049 | 0.000 | *** |
| responsibility | time | 0.064 | 0.040 | 0.113 | |
| responsibility | group | -0.018 | 0.068 | 0.797 | |
| responsibility | time*group | -0.060 | 0.056 | 0.289 | |
| anxiety | Intercept | 3.396 | 0.053 | 0.000 | *** |
| anxiety | time | -0.035 | 0.038 | 0.355 | |
| anxiety | group | 0.047 | 0.075 | 0.534 | |
| anxiety | time*group | -0.036 | 0.053 | 0.498 | |
| depression | Intercept | 2.922 | 0.058 | 0.000 | *** |
| depression | time | -0.076 | 0.039 | 0.052 | |
| depression | group | -0.013 | 0.081 | 0.876 | |
| depression | time*group | 0.004 | 0.054 | 0.941 | |
| volatility | Intercept | 2.886 | 0.056 | 0.000 | *** |
| volatility | time | -0.070 | 0.038 | 0.069 | |
| volatility | group | -0.078 | 0.078 | 0.322 | |
| volatility | time*group | 0.072 | 0.054 | 0.181 | |
| curiosity | Intercept | 3.865 | 0.046 | 0.000 | *** |
| curiosity | time | 0.029 | 0.033 | 0.374 | |
| curiosity | group | -0.015 | 0.064 | 0.811 | |
| curiosity | time*group | -0.048 | 0.046 | 0.300 | |
| aesthetic | Intercept | 3.724 | 0.051 | 0.000 | *** |
| aesthetic | time | -0.025 | 0.035 | 0.486 | |
| aesthetic | group | -0.060 | 0.072 | 0.401 | |
| aesthetic | time*group | 0.031 | 0.050 | 0.534 | |
| imagination | Intercept | 3.786 | 0.047 | 0.000 | *** |
| imagination | time | -0.007 | 0.034 | 0.834 | |
| imagination | group | -0.037 | 0.065 | 0.575 | |
| imagination | time*group | 0.065 | 0.048 | 0.177 |
Changes in ideal self traits over time:
df_manip_check_ideal_fu <- df_sbsa2_t2t3 %>%
select(pid, time_d, rando,
paste0(str_trunc(names(b5_vars), 5, ellipsis = ""), "_comb_ideal")) %>%
pivot_longer(ends_with("_comb_ideal"),
names_to = "test", names_prefix = "facet", values_to = "score", values_drop_na = TRUE) %>%
group_nest(test) %>%
mutate(lmer_mods = map(data, ~lmerTest::lmer(score ~ time_d * rando + (1 | pid), data = .x))) %>%
pull(lmer_mods) %>%
purrr::set_names(sort(names(b5_vars))) # ordered alphabetically
df_manip_check_ideal_fu_unlist <- as.data.frame(summary(df_manip_check_ideal_fu[[1]])$coefficients) %>% as_tibble()
for (i in 2:length(df_manip_check_ideal_fu)) {
df_manip_check_ideal_fu_unlist <- bind_rows(df_manip_check_ideal_fu_unlist,
as.data.frame(summary(df_manip_check_ideal_fu[[i]])$coefficients) %>% as_tibble())
}kable(df_manip_check_ideal_fu_unlist %>%
mutate(outcome = rep(sort(names(b5_vars)), each=4),
term = c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%
rename(p = `Pr(>|t|)`, str_err = `Std. Error`, est = Estimate) %>% select(outcome, term, est, str_err, p) %>%
mutate(sig = ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%
arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits,
digits = 3)| outcome | term | est | str_err | p | sig |
|---|---|---|---|---|---|
| extraversion | Intercept | 3.897 | 0.029 | 0.000 | *** |
| extraversion | time | -0.006 | 0.024 | 0.808 | |
| extraversion | group | 0.000 | 0.041 | 0.997 | |
| extraversion | time*group | -0.016 | 0.034 | 0.649 | |
| agreeableness | Intercept | 4.099 | 0.032 | 0.000 | *** |
| agreeableness | time | 0.003 | 0.024 | 0.891 | |
| agreeableness | group | -0.072 | 0.045 | 0.108 | |
| agreeableness | time*group | 0.033 | 0.033 | 0.327 | |
| conscientiousness | Intercept | 4.448 | 0.030 | 0.000 | *** |
| conscientiousness | time | -0.007 | 0.023 | 0.761 | |
| conscientiousness | group | -0.022 | 0.042 | 0.605 | |
| conscientiousness | time*group | 0.006 | 0.032 | 0.848 | |
| neuroticism | Intercept | 1.700 | 0.029 | 0.000 | *** |
| neuroticism | time | 0.010 | 0.025 | 0.675 | |
| neuroticism | group | 0.012 | 0.041 | 0.769 | |
| neuroticism | time*group | -0.011 | 0.035 | 0.760 | |
| openness | Intercept | 4.192 | 0.033 | 0.000 | *** |
| openness | time | 0.004 | 0.026 | 0.864 | |
| openness | group | -0.054 | 0.047 | 0.250 | |
| openness | time*group | 0.006 | 0.037 | 0.866 | |
| sociability | Intercept | 3.661 | 0.042 | 0.000 | *** |
| sociability | time | 0.003 | 0.036 | 0.923 | |
| sociability | group | 0.029 | 0.059 | 0.615 | |
| sociability | time*group | -0.054 | 0.050 | 0.284 | |
| assertiveness | Intercept | 3.826 | 0.040 | 0.000 | *** |
| assertiveness | time | -0.021 | 0.039 | 0.580 | |
| assertiveness | group | 0.010 | 0.057 | 0.863 | |
| assertiveness | time*group | -0.024 | 0.054 | 0.655 | |
| energy | Intercept | 4.203 | 0.037 | 0.000 | *** |
| energy | time | 0.004 | 0.040 | 0.927 | |
| energy | group | -0.044 | 0.052 | 0.397 | |
| energy | time*group | 0.034 | 0.056 | 0.541 | |
| compassion | Intercept | 4.070 | 0.041 | 0.000 | *** |
| compassion | time | 0.035 | 0.038 | 0.354 | |
| compassion | group | -0.057 | 0.058 | 0.327 | |
| compassion | time*group | 0.019 | 0.053 | 0.726 | |
| respectfulness | Intercept | 4.454 | 0.035 | 0.000 | *** |
| respectfulness | time | -0.005 | 0.029 | 0.865 | |
| respectfulness | group | -0.044 | 0.049 | 0.372 | |
| respectfulness | time*group | 0.027 | 0.041 | 0.518 | |
| trust | Intercept | 3.774 | 0.043 | 0.000 | *** |
| trust | time | -0.021 | 0.037 | 0.568 | |
| trust | group | -0.112 | 0.061 | 0.066 | |
| trust | time*group | 0.054 | 0.052 | 0.296 | |
| organization | Intercept | 4.434 | 0.034 | 0.000 | *** |
| organization | time | 0.037 | 0.034 | 0.281 | |
| organization | group | 0.038 | 0.048 | 0.428 | |
| organization | time*group | -0.028 | 0.048 | 0.557 | |
| productiveness | Intercept | 4.564 | 0.033 | 0.000 | *** |
| productiveness | time | -0.049 | 0.032 | 0.121 | |
| productiveness | group | -0.008 | 0.046 | 0.865 | |
| productiveness | time*group | 0.015 | 0.044 | 0.732 | |
| responsibility | Intercept | 4.346 | 0.038 | 0.000 | *** |
| responsibility | time | 0.001 | 0.034 | 0.984 | |
| responsibility | group | -0.076 | 0.053 | 0.154 | |
| responsibility | time*group | 0.024 | 0.048 | 0.614 | |
| anxiety | Intercept | 1.901 | 0.038 | 0.000 | *** |
| anxiety | time | -0.002 | 0.039 | 0.953 | |
| anxiety | group | -0.020 | 0.053 | 0.704 | |
| anxiety | time*group | 0.053 | 0.055 | 0.336 | |
| depression | Intercept | 1.490 | 0.032 | 0.000 | *** |
| depression | time | -0.029 | 0.031 | 0.350 | |
| depression | group | 0.021 | 0.044 | 0.637 | |
| depression | time*group | 0.003 | 0.043 | 0.949 | |
| volatility | Intercept | 1.708 | 0.038 | 0.000 | *** |
| volatility | time | 0.061 | 0.035 | 0.085 | |
| volatility | group | 0.036 | 0.053 | 0.499 | |
| volatility | time*group | -0.092 | 0.050 | 0.065 | |
| curiosity | Intercept | 4.157 | 0.042 | 0.000 | *** |
| curiosity | time | -0.008 | 0.039 | 0.847 | |
| curiosity | group | -0.052 | 0.059 | 0.378 | |
| curiosity | time*group | -0.033 | 0.055 | 0.548 | |
| aesthetic | Intercept | 4.034 | 0.044 | 0.000 | *** |
| aesthetic | time | -0.011 | 0.037 | 0.776 | |
| aesthetic | group | -0.135 | 0.061 | 0.028 | * |
| aesthetic | time*group | 0.080 | 0.052 | 0.125 | |
| imagination | Intercept | 4.385 | 0.038 | 0.000 | *** |
| imagination | time | 0.037 | 0.036 | 0.315 | |
| imagination | group | 0.022 | 0.053 | 0.682 | |
| imagination | time*group | -0.029 | 0.051 | 0.575 |